{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f9be2da",
   "metadata": {},
   "source": [
    "# Credit Card Default Risk Analysis and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddfcb4ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Consolidated import statements for the entire project\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Model building and preprocessing\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Models\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from statsmodels.discrete.discrete_model import MNLogit\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "\n",
    "# Imbalanced learning\n",
    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "\n",
    "# Evaluation metrics and visualization\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, recall_score, precision_score,\n",
    "    f1_score, roc_auc_score, classification_report,\n",
    "    confusion_matrix, ConfusionMatrixDisplay, roc_curve\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecdc85f",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 1. Project Overview\n",
    "\n",
    "### 1.1 Background\n",
    "In today’s digital financial era, credit cards have become ubiquitous tools for consumption and lending. However, with the expansion of credit card services, default risk has become a growing challenge for financial institutions.\n",
    "\n",
    "Traditional credit scoring systems, often based on logistic regression, struggle to adapt to the evolving financial environment and massive datasets. While machine learning methods like Random Forests and Neural Networks offer higher predictive power, their \"black box\" nature poses challenges in regulatory transparency.\n",
    "\n",
    "This project focuses on identifying the most influential features affecting credit risk, employing both econometric analysis and machine learning models to develop effective prediction strategies that can inform lending decisions in the credit card industry.\n",
    "\n",
    "### 1.2 Data Sources\n",
    "\n",
    "This project is based on the Kaggle dataset \"Credit Card Approval Prediction\" ([ link ](https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction/data)), which includes:\n",
    "- `credit_application.csv` : 438,558 records of customer demographics\n",
    "- `credit_record.csv` : 1,048,576 records of credit behavior\n",
    "\n",
    "### 1.3 Research Framework\n",
    "Inspired by classic data analysis projects, this project is structured around the following key components:\n",
    "\n",
    "- **Data Preprocessing**: Cleaning, transforming, and merging raw datasets to prepare them for analysis.\n",
    "- **Data Visualization**: Generating charts and plots to understand the distribution and relationships among variables.\n",
    "- **Logistic Regression Analysis**: Applying a multinomial logistic regression model to interpret the influence of each feature on credit classification.\n",
    "- **Random Forest Classification**: Leveraging an ensemble tree-based model to improve predictive performance and assess feature importance.\n",
    "- **Neural Network Classification**: Implementing a multilayer perceptron to explore complex, nonlinear relationships in the data.\n",
    "- **Conclusion and Recommendations**: Summarizing key findings and proposing practical strategies for improving credit risk evaluation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58132c62",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 2. Data Preprocessing\n",
    "\n",
    "### 2.1 `credit_application.csv`\n",
    "\n",
    "#### **Preview :**\n",
    "\n",
    "This file mainly contains information about customers' IDs, demographic characteristics, income status, and family conditions. It includes 0-1 binary classification features, multi-classification features, and continuous numerical features.\n",
    "\n",
    "#### **Feature Summary :**\n",
    "| Feature | Description |\n",
    "|---------|-------------|\n",
    "| ID | Customer ID |\n",
    "| CODE_GENDER | Gender |\n",
    "| FLAG_OWN_CAR | Owns a car |\n",
    "| FLAG_OWN_REALTY | Owns property |\n",
    "| CNT_CHILDREN | Number of children |\n",
    "| AMT_INCOME_TOTAL | Annual income |\n",
    "| NAME_INCOME_TYPE | Type of income |\n",
    "| NAME_EDUCATION_TYPE | Education level |\n",
    "| NAME_FAMILY_STATUS | Marital status |\n",
    "| NAME_HOUSING_TYPE | Housing condition |\n",
    "| DAYS_BIRTH | Age ( in days, negative ) |\n",
    "| DAYS_EMPLOYED | Days employed ( if working, negative ) |\n",
    "| FLAG_MOBIL | Has mobile phone |\n",
    "| FLAG_WORK_PHONE | Has work phone |\n",
    "| FLAG_PHONE | Has phone |\n",
    "| FLAG_EMAIL | Has email |\n",
    "| OCCUPATION_TYPE | Occupation |\n",
    "| CNT_FAM_MEMBERS | Family size |\n",
    "\n",
    "#### **Processing Steps :**\n",
    "\n",
    "1. **Data Type Inspection**: All variables were verified to have appropriate data types according to their nature (e.g., `categorical`, `numerical`, `binary`).\n",
    "\n",
    "2. **Missing Value Handling**: A missing value analysis revealed that only the feature `OCCUPATION_TYPE` contained null entries. Given that occupation is not easily imputed and the dataset is large enough, we chose to drop all records with missing values in this feature.\n",
    "\n",
    "3. **Variable Transformation**: Binary variables were converted to numerical format (0 and 1). The variables `DAYS_BIRTH` and `DAYS_EMPLOYED` were transformed into positive integers to reflect age and tenure more intuitively.\n",
    "\n",
    "4. **Removal of Redundant Features**: The feature `FLAG_MOBIL` was dropped as it contained only one unique value \"1\" across all observations and thus had no analytical value.\n",
    "\n",
    "5. **Outlier Detection and Removal**: For continuous numerical variables, boxplots were used to visualize the distribution. Outliers were subsequently removed based on the 3-sigma rule, enhancing the robustness of the data and the stability of the models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5f1fba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset\n",
    "application = pd.read_csv(\"application_record.csv\")\n",
    "status = pd.read_csv(\"credit_record.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8ccbe1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 438557 entries, 0 to 438556\n",
      "Data columns (total 18 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   ID                   438557 non-null  int64  \n",
      " 1   CODE_GENDER          438557 non-null  object \n",
      " 2   FLAG_OWN_CAR         438557 non-null  object \n",
      " 3   FLAG_OWN_REALTY      438557 non-null  object \n",
      " 4   CNT_CHILDREN         438557 non-null  int64  \n",
      " 5   AMT_INCOME_TOTAL     438557 non-null  float64\n",
      " 6   NAME_INCOME_TYPE     438557 non-null  object \n",
      " 7   NAME_EDUCATION_TYPE  438557 non-null  object \n",
      " 8   NAME_FAMILY_STATUS   438557 non-null  object \n",
      " 9   NAME_HOUSING_TYPE    438557 non-null  object \n",
      " 10  DAYS_BIRTH           438557 non-null  int64  \n",
      " 11  DAYS_EMPLOYED        438557 non-null  int64  \n",
      " 12  FLAG_MOBIL           438557 non-null  int64  \n",
      " 13  FLAG_WORK_PHONE      438557 non-null  int64  \n",
      " 14  FLAG_PHONE           438557 non-null  int64  \n",
      " 15  FLAG_EMAIL           438557 non-null  int64  \n",
      " 16  OCCUPATION_TYPE      304354 non-null  object \n",
      " 17  CNT_FAM_MEMBERS      438557 non-null  float64\n",
      "dtypes: float64(2), int64(8), object(8)\n",
      "memory usage: 60.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# 2.1.1 Data Type Inspection\n",
    "application.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f548c3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                          0\n",
       "CODE_GENDER                 0\n",
       "FLAG_OWN_CAR                0\n",
       "FLAG_OWN_REALTY             0\n",
       "CNT_CHILDREN                0\n",
       "AMT_INCOME_TOTAL            0\n",
       "NAME_INCOME_TYPE            0\n",
       "NAME_EDUCATION_TYPE         0\n",
       "NAME_FAMILY_STATUS          0\n",
       "NAME_HOUSING_TYPE           0\n",
       "DAYS_BIRTH                  0\n",
       "DAYS_EMPLOYED               0\n",
       "FLAG_MOBIL                  0\n",
       "FLAG_WORK_PHONE             0\n",
       "FLAG_PHONE                  0\n",
       "FLAG_EMAIL                  0\n",
       "OCCUPATION_TYPE        134203\n",
       "CNT_FAM_MEMBERS             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.1.2 Missing Value Handling\n",
    "application.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75d7adbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "application.dropna(subset=\"OCCUPATION_TYPE\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35013be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.1.3 Variable Transformation\n",
    "\n",
    "# (1) convert Binary variables to numerical format (0,1)\n",
    "application[\"CODE_GENDER\"] = application[\"CODE_GENDER\"].apply(lambda x:{\"M\":0,\"F\":1}[x])\n",
    "application = application.rename(columns={\"CODE_GENDER\":\"FLAG_FEMALE\"})\n",
    "application[\"FLAG_OWN_CAR\"] = application[\"FLAG_OWN_CAR\"].apply(lambda x:{\"Y\":1,\"N\":0}[x])\n",
    "application[\"FLAG_OWN_REALTY\"] = application[\"FLAG_OWN_REALTY\"].apply(lambda x:{\"Y\":1,\"N\":0}[x])\n",
    "\n",
    "# (2) transforme the variables `DAYS_BIRTH` and `DAYS_EMPLOYED` into positive integers\n",
    "application[\"DAYS_BIRTH\"] = -application[\"DAYS_BIRTH\"]\n",
    "application[\"DAYS_EMPLOYED\"] = -application[\"DAYS_EMPLOYED\"]\n",
    "\n",
    "# 2.1.4 Removal of Redundant Features\n",
    "application = application.drop(columns=\"FLAG_MOBIL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1a46de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.1.5 Outlier Detection and Removal\n",
    "\n",
    "# (1) Mark numeric variables\n",
    "numeric_cols = [\"CNT_CHILDREN\", \"AMT_INCOME_TOTAL\", \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"CNT_FAM_MEMBERS\"] \n",
    "\n",
    "# (2) Boxplot\n",
    "fig, axes = plt.subplots(1, len(numeric_cols), figsize=(12, 4))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    sns.boxplot(y=application[col], ax=axes[i], color=\"skyblue\")\n",
    "    axes[i].set_title(col, fontsize=10)\n",
    "    axes[i].set_xlabel(\"\")\n",
    "plt.suptitle(\"Numeric Feature Box Plots (Before Outlier Removal)\", fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# (3) Define a function to remove samples with outliers using the 3-sigma rule\n",
    "def remove_outliers(df, cols):\n",
    "    for col in cols:\n",
    "        mean = df[col].mean()\n",
    "        std = df[col].std()\n",
    "        df = df[(df[col] >= mean - 3 * std) & (df[col] <= mean + 3 * std)]\n",
    "    return df\n",
    "\n",
    "application = remove_outliers(application, numeric_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91618625",
   "metadata": {},
   "source": [
    "### 2.2 `credit_record.csv`\n",
    "\n",
    "#### **Preview :**\n",
    "This file mainly contains the monthly credit status of customers within a certain period. The credit status `STATUS` is divided into 8 types according to repayment status, non-loan status, and overdue duration. The variable `MONTHS` represents time, recorded in reverse chronological order from the reference point of observation.\n",
    "\n",
    "#### **Feature Summary :**\n",
    "| Feature | Description |\n",
    "|--------|-------------|\n",
    "| ID | Client number |\n",
    "| MONTHS_BALANCE | Months since the record ( 0 = current month, -1 = previous ) |\n",
    "| STATUS | Credit status: <br> 0: 1-29 days overdue <br> 1: 30-59 days overdue <br> 2: 60-89 days overdue <br> 3: 90-119 days overdue <br> 4: 120-149 days overdue <br> 5: 150+ days overdue / bad debt <br> C: Paid off that month <br> X: No loan for the month |\n",
    "\n",
    "#### **Data Preprocessing :**\n",
    "1. The `STATUS` column contains values with leading or trailing spaces, which need to be removed to ensure consistency.\n",
    "\n",
    "2. Since the `STATUS` column includes categorical values such as \"C\" and \"X\", which are not suitable for quantitative analysis, the following mapping is applied to convert the values into numeric format:\n",
    "    - C → -1\n",
    "    - X → 0\n",
    "    - 0 → 1\n",
    "    - 1 → 2\n",
    "    - 2 → 3\n",
    "    - 3 → 4\n",
    "    - 4 → 5\n",
    "    - 5 → 6\n",
    "\n",
    "#### **Credit Label Construction :**\n",
    "Although the variable `STATUS` in the original dataset has a straightforward meaning, the classification is too detailed and not suitable for qualitative and quantitative analysis, so certain dimensionality reduction and transformation are required.\n",
    "\n",
    "According to the 2025 regulations in China, overdue payments are clearly divided into mild (1-30 days), moderate (31-90 days), and severe (more than 90 days). Overdue payments exceeding 90 days will be included in non-performing loan management, and banks can take legal measures for recovery. \n",
    "\n",
    "Therefore, I divide the sample `credit` into three categories:\n",
    "- **Good :** On-time or no loans\n",
    "- **Medium :** 1-3 months overdue records\n",
    "- **Bad :** Over 3 months overdue records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6120f951",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.2.1 Data Preprocessing\n",
    "\n",
    "# (1) Delete space\n",
    "status[\"STATUS\"] = status[\"STATUS\"].str.replace(\" \",\"\")\n",
    "\n",
    "# (2) Map the values of \"STATUS\"\n",
    "def status_map(status):\n",
    "    smap = {\"C\":-1,\n",
    "            \"X\":0,\n",
    "            \"0\":1,\n",
    "            \"1\":2,\n",
    "            \"2\":3,\n",
    "            \"3\":4,\n",
    "            \"4\":5,\n",
    "            \"5\":6}\n",
    "    return smap.get(status)\n",
    "\n",
    "status[\"STATUS_MAP\"] = status[\"STATUS\"].apply(status_map)\n",
    "\n",
    "# 2.2.2 Credit Label Construction\n",
    "\n",
    "# (1) Count the number of occurrences of each delinquency level (-1 to 6) per customer\n",
    "credit = status.groupby(\"ID\")[\"STATUS_MAP\"].value_counts().unstack(\"STATUS_MAP\").fillna(0)\n",
    "\n",
    "# (2) Label credit status: \n",
    "# - Delinquency of 3+ months (values 4–6) → \"bad\"\n",
    "# - Delinquency of 1–3 months (values 1–3) → \"medium\"\n",
    "# - No loan or on-time repayment (values -1, 0) → \"good\"\n",
    "credit[\"credit\"] = \"good\" \n",
    "credit.loc[credit[[1,2,3]].any(axis=1), \"credit\"] = \"medium\"\n",
    "credit.loc[credit[[4,5,6]].any(axis=1), \"credit\"] = \"bad\"\n",
    "credit = credit[\"credit\"].reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2723d683",
   "metadata": {},
   "source": [
    "### 2.3 Dataset Merging\n",
    "\n",
    "We merge the processed \"credit\\_application\" dataset and \"credit\\_record\" dataset through the \"ID\" indicator to obtain the \"credit\\_application\" dataset. This dataset has a total of 122,791 samples, and no further data processing is required after merging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a90871a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.3 Dataset Merging\n",
    "credit_application = pd.merge(application,credit,on=\"ID\",how=\"inner\")\n",
    "credit_application = credit_application.set_index(\"ID\")\n",
    "\n",
    "# Save the dataset \"credit_application.csv\" for quick subsequent calls\n",
    "credit_application.to_csv(\"credit_application.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23927518",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "## 3. Data Visualization\n",
    "\n",
    "### 3.1 Binary and Categorical Features\n",
    "\n",
    "![catbin_stacked_bar](b&c_bar.png)\n",
    "\n",
    "#### (1) Socioeconomic Status Indicators\n",
    "\n",
    "- Individuals classified as \"Working\" or \"Commercial associate\" under the `NAME_INCOME_TYPE` feature dominate the dataset, suggesting that most applicants are engaged in formal employment. \n",
    "\n",
    "- The `NAME_EDUCATION_TYPE` feature is highly concentrated around \"Secondary / secondary special\" education, with fewer individuals holding higher degrees. This implies a working-class applicant base with mid-level education. \n",
    "\n",
    "- The `OCCUPATION_TYPE` variable shows a relatively broad spread, though \"Laborers\" and \"Core staff\" are the most frequent, indicating a high share of manual and mid-tier service occupations. \n",
    "\n",
    "- The `NAME_FAMILY_STATUS` feature is skewed towards \"Married\" applicants, suggesting a prevalence of individuals with family responsibilities and potentially more financial stability.\n",
    "\n",
    "#### (2) Asset Ownership and Financial Capacity\n",
    "\n",
    "- According to the chart, a majority of clients report `FLAG_OWN_REALTY`, which suggests relatively stable living conditions. \n",
    "- `FLAG_OWN_CAR` is more balanced, indicating heterogeneity in disposable income. \n",
    "\n",
    "\n",
    "#### (3) Communication Accessibility\n",
    "\n",
    "- While most users report having a personal phone, work phone access and email usage are slightly less prevalent. This may reflect employment sector differences—formal white-collar employees are more likely to report multiple channels. \n",
    "\n",
    "#### (4) Demographic and Household Structure\n",
    "\n",
    "- The dataset includes a slight female majority in `FLAG_FEMALE`, which aligns with the finding that women often engage more cautiously with credit. \n",
    "- The number of children `CNT_CHILDREN` and household size `CNT_FAM_MEMBERS` both exhibit right-skewed distributions, with most individuals having one or no children and small-to-medium-sized families. These characteristics influence the assessment of financial pressure and repayment flexibility.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4abc9c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pVL4sr3Mjr2PdunVr2NvbIygoCNOnT4dYLIZEIsFff/2FsmXLokmTJoWKP1dMTEyeE51fvXoVUqkUEolE5YTejx8/BgCEhISgbdu2AHKSCb///js2b96MkJAQpKSkQBAE+Trv8174UGXKlFE58X7ue+vQoUMqk2NaWlp5Dnn9EG/vd34aNmwIa2trzJs3D7du3UKbNm1Qv359eHp65jv/U17y2v+CNGjQQKnM1tYWLi4uCAkJQXJycqHmaSsKISEhSE9Ph4+Pj8q78vn4+ODw4cO4deuW/I6auQp6b+bq0qULVq5cCQ8PD3Tt2hWNGjVCnTp1YGBgULQ7Q0RUQpiUIiIi+sxYW1sjJCQEUVFRKF++fKHXGzlyJH7//Xc4ODigffv2sLGxkc85NWPGDGRkZBS6rfj4eADA8uXL862XmpoKS0tL+STIpUqVUqqjal6s3Pp5zZmVm6BITEwsVHt5yf3yZ2dnV2Ddbdu2oWvXrjA0NISvry+cnZ2hr68vn+T62bNnhdrmnj17AADffPNNgXU1NDTg6OiIKVOm4OHDh9iwYQM2bdpU6PmYVM0Dldvj4u2k06JFizB27FiUKlUKLVq0gL29vbxHzNKlS/M8N/I61hoaGujfvz9mzJiBQ4cOoXXr1ti+fTsSEhIwbty4D0pi5CX3XDx//jzOnz+fZ73U1FT5/7/77jvs27cP5cqVQ9euXWFlZQUtLS0kJCRg2bJl7/Ve+FB5Hbvc/ckraVnUYmJiAKh+b77NxMQEFy9exLRp07Bv3z4cPHgQQE4iZdKkSRg6dOh7bfd93qdvy2tOtdKlSyMkJARJSUlqS0p9zOdUYd+bv/76K8qWLYvg4GDMnj0bs2fPhq6uLrp06YJFixZ9UncUJSL6EExKERERfWbq1auHU6dO4fjx44XucfLq1SssX74clStXxsWLFxV+1X/x4oXCXfoKI3dI2J07d+Dh4VHo+q9fv1aaDPzly5d51le17O1yVRMjv0/CI3cS5qioKKWJmN81ffp06Orq4vr163Bzc1NY9vZQxYLs2bMHLi4ucHd3L/Q6QE6vpA0bNuDGjRtFOkl47h36bG1tcevWLYXkhCAIWLhwYZ7r5nesv//+e8yePRt//PEHWrdujT/++AOampp53g3tQ+WeA2PGjMEvv/xSYP2rV69i37598PX1xYEDBxSGj126dAnLli17r+2LxTmzYbzday6XqmRErryOXe7+qCu5knvHuxo1ahRYN/fOelKpFHfu3MGRI0fw66+/YtiwYTAzM4Ofn1+ht/uhiUlVd4MElD8TPvR1eR8f8zlVWFpaWhg3bhzGjRuH6OhonD59GkFBQVi/fj1evHiBw4cPf3DbRESfAs4pRURE9Jnx9/eHhoYGVq9ejdevX+dbN7fHR1hYGARBQLNmzZSGmZw9e1blurlf1t/+1T5XrVq1AAAXL14sVMxeXl4AoHJuFVVluXe8OnPmjNLwIkEQ5DEXdGesguQOPzty5EiBdZ88eYKKFSsqJaSio6Px5MmTQm0vPj4e586dK1QvKVXrAlC6ZfzHio2NRWJiImrXrq3UW+batWtIT0//oHbt7e3RqlUr+Z0Sz5w5g9atW6ucR+tj1KhRAyKRqNDnYu5r1aZNG6X5jPJ7L6h6HwCQ32EyKipKaVnuMNT3kfveyh3GV5xev36NVatWAQC6detW6PU0NDTg7e2N8ePHy+e32rt3r8JyQPVnx8dS9RrlvgddXFzkiby3E87vyut1ed+4K1SoAF1dXVy9ehVpaWlKy0+fPg3g4z+nctna2sLPzw+HDh2Cm5sbjh079sHvTyKiTwWTUkRERJ8ZV1dXjB8/HrGxsWjVqhWePn2qVEcikWDx4sXyOXZyeydduHBBIanx/Plz+fxF78qdRPf58+dKy/r27QsjIyNMmTIF9+7dU1qelpam8KW6W7duEIvFWLx4MeLi4uTlqampKocpOTo6onHjxrh3757SZNlr167FvXv30KRJE6X5pN5Xnz59YGhoiEWLFuHWrVtKy9/+Quvk5ITQ0FCFXhESiQRDhgxR2RtDlQMHDiA7O/u9k1KJiYkIDg4GoHpOnY9hZWUFPT093LhxQ+GL9Zs3bzBixIiPanvQoEHIyspCly5dIAjCe09wXhjW1tbo0qULLly4gP/9738q50i6fPmyfN9y3wvnzp1TqHPv3j3MmzdP5TbMzc0RGxsLiUSitKxq1aoQiUTYvHmzwvLHjx+/d68rABg6dCg0NTUxYsQIlRP5JyQkfFCy6113795FixYt8OrVK/j7+xc4if7du3dVDlHNfT+8PQF6fp8dH+vo0aM4fvy4QtnUqVORlZWFPn36yMuMjY1Rrlw5nDt3TmHeu+TkZEyaNEll2+8bt7a2Nvz8/BAbG6t07hw7dgx///03XF1dUa9evUK1966MjAycOHFC6ZxOTU1FcnIytLS03nuieCKiTw2H7xEREX2GZs+eDYlEgiVLlqB8+fJo0qQJPDw8oKWlhadPn+LYsWOIi4vD7NmzAQA2Njb49ttvsWPHDlSvXh1NmzbFy5cvsX//fjRp0gRhYWFK22jSpAm2b9+Ozp07o3Xr1tDV1YWnpyfatGkjvwNY586d4eXlhZYtW6JChQqQSCR49uwZTp8+jbp16+LQoUMAcu76NnHiRMydOxeenp7o3LkzNDU1sXPnTnh6euLu3bvy4Ta5VqxYgfr162PAgAHYt28fKlWqhPv372Pv3r0oVaoUVqxY8dHH0crKCuvXr0e3bt1Qs2ZNtG/fHuXLl0dsbCwuX74MZ2dn7N69GwAwYsQIjBgxAlWqVMF3332H7OxsHD16FIIgwMvLSz6Ze3727NkDCwuLfL+kHjt2TJ7ckMlkiIqKwt69exEbG4vmzZvj22+//ej9fptYLMbQoUOxaNEieHl5oV27dkhKSsLff/8NJyenj+rZ1Lp1azg4OCAyMhJ2dnZo1apVEUb+n4CAADx8+BDjx4/Hhg0bUKdOHZiYmCAyMhLXr1/H48ePERMTA319fdSsWRM1a9bE1q1bERMTg9q1ayMiIgJ79+5FmzZtsH37dqX2mzRpgmvXrqFdu3Zo0KABtLW1Ub9+fdSvXx92dnbo2rUrNm/ejGrVqqFly5Z49eoVdu3ahZYtW2LHjh3vtS8eHh4ICAjAkCFDUL58ebRu3RouLi5ISkpCWFgYTp8+DX9/f/ndBwsSGxsrT05nZ2cjLi4O169fx9WrVwHkDLMsaG44IOe8HDNmDOrVq4cKFSrAwsICYWFh2Lt3L/T09OR38cw9Xr/88gsGDRqEzp07w8DAAI6Ojujevft7HQtV2rRpg9atW6Nz585wcHDA6dOncfHiRXh5eWHs2LEKdX/88UcMHjwYderUQefOnSGTyfD333/nmYDL7zMvLwsWLMDp06cxe/ZsXLhwAbVq1UJ4eDi2b98OfX19BAUFKX22FVZ6ejqaNm2KsmXLolatWnB0dERKSgr279+PFy9eYMKECe9910Miok+OQERERJ+tq1evCv369RNcXV0FPT09QUdHR3B2dhb8/PyEI0eOKNRNTk4WxowZIzg7Ows6OjqCm5ubMGvWLCEzM1MAIDRq1EihflZWljB+/HjB0dFR0NTUFAAIffr0UagTEhIi9O/fX3BychK0tbUFMzMzwdPTUxg5cqRw5coVpXgDAgKEihUrCtra2oK9vb0wduxYITIyUgAgfPPNN0r1w8PDhb59+wo2NjaCpqamYGNjI/Tt21cIDw9XqtuoUSMhv0ubadOmCQCEkydPKi27efOm0KVLF6F06dKClpaWYGNjI7Rq1UrYv3+/vI5MJhNWrlwpuLu7C7q6uoK1tbXQv39/4eXLlyq3HRQUJAAQgoKCBEEQBIlEIhgaGiodw3frv/swNDQUatasKSxZskTIzMxUWOfp06cqX5f8jkWfPn0EAMLTp0/lZZmZmcKcOXMENzc3QUdHR3B0dBR+/PFHITk5WXBychKcnJwKbCMvkyZNEgAIU6dOLbDuu3L3IyYmpsC6aWlpwsKFC4Vq1aoJBgYGgp6enlCmTBmhQ4cOwvr164WsrCx53VevXgn9+vUTbG1tBV1dXcHT01NYvny5EBYWpvJ4JicnCwMGDBBsbGwEsVgsABCmTZsmX56amiqMGDFCKF26tKCjoyNUrlxZ2Lhxo3Dy5EmluoIgqHy/vevKlStCt27dBFtbW0FLS0uwtLQUqlatKkycOFF48OBBgccjdztvP3R0dAQrKyuhXr16wtixY4Xbt2+rXE/VeXX//n1h1KhRQpUqVQQLCwtBR0dHKFu2rODv7y/cv39fqY2FCxcKbm5ugpaWltL+FrT/Bb2fdu7cKVSrVk3Q1dUVrKyshEGDBglxcXEq2/rtt98EV1dXQUtLS3B0dBR+/vnnD/7Myyvu169fCyNHjhScnJzkr9V3330n3LlzR6lufu+ddz+jMjMzhQULFggtWrQQ7O3tBW1tbaF06dJCo0aNhM2bN+d5/IiIPiciQSjkfWCJiIiIisGxY8fQvHlzjB8/HgsWLCjpcIrNwYMH0aZNG+zcuRMdO3Ys6XDUpnXr1jh06BDCwsIKnEyeiIiIvi6cU4qIiIjU4vXr10oTCCckJMjnd+nQoUMJRKU+e/bsga6uLlq0aFHSoajNvXv3cOjQIbRs2ZIJKSIiIlLCnlJERESkFkuXLsUvv/yCJk2awNbWFjExMTh06JB8ouWgoKCSDpGKyF9//YWHDx9i/fr1ePbsGc6fP486deqUdFhERET0ieFE50RERKQWdevWRbVq1XDs2DHEx8dDQ0MDFStWxE8//YShQ4eWdHhUhFavXo2zZ8/CyckJgYGBTEgRERGRSuwpRUREREREREREasc5pYiIiIiIiIiISO2YlCIiIiIiIiIiIrXjnFJEH0kmkyE6OhpGRkYQiUQlHQ4RERERERFRkRMEAcnJybC1tYVYXDR9nJiUIvpI0dHRcHBwKOkwiIiIiIiIiIpdZGQk7O3ti6QtJqWIPpKRkRGAnDemsbFxCUdDREREREREVPSSkpLg4OAg/w5cFJiUIvpIuUP2jI2NmZQiIiIiIiKiL1pRTlvDic6JiIiIiIiIiEjtmJQiIiIiIiIiIiK1Y1KKiIiIiIiIiIjUjkkpIiIiIiIiIiJSOyaliIiIiIiIiIhI7ZiUIiIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjsmpYiIiIiIiIiISO2YlCIiIiIiIiIiIrVjUoqIiIiIiIiIiNSOSSkiIiIiIiIiIlI7JqWIiIiIiIiIiEjtmJQiIiIiIiIiIiK1Y1KKiIiIiIiIiIjUjkkpIiIiIiIiIiJSOyaliIiIiIiIiIhI7ZiUIiIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjsmpYi+cjJBVvRtyoQib/NdglD826DPm0xW9Of210p461gKanh/q1PuZ4lQDJ+FRFT8pPlcD0i/sM+rr40gk5Z0CIUmSD8sVoW/r7y2pTwIeVzTFuc1mTqvizTVtiWiL939HYAopaSjeD+21SF2qIPjr/cjISuuSJr0Mq4JV8OKOLr2HuJjUoukzXc5eVig9jcueHT9OdKTM4tlG/R5M7UygFOl0jjw2y+Ij4os6XA+a87e1dCgW288OnUUOoZGcKpeG3GbQ5D9Kq2kQ/toho3sYeBlhbi4OFhYWODFi4PIyoov6bCIqJD09JxhaVkfQ+8/w+NUicIyPxtz9LMvhVGbbyL01Wd2fUboUt0efeqWAXZ8D8Q+Kulw8td0GkSuTRE1dhwyw8IKvZpBgwaw+mE0cPwYULo0RB6evLYlJVaOJrApa4FHp44iLeG/axQze6diuyYz+cYFuk4mKq+LRKIKRbotgEkpoqKT9hqQvSnpKN6PJAkAkJAVh9jMV0XTpCwdABAfk4rYyOK5CDSz1gcApCdnIjVRUkBt+hrpGWoDAOKjIvHq6ZMSjubzZm5rDwBIS4iX/4qb/SoNWdHFk3RWJ1lqFgAgKyv333hkZBTNZyERFT8tLXMAwONUCe6kpCssa5yR874OfZWCe9FJao+NPs7LpH+v72IfATG3SzaYgkgSAACZYWGQ3L9f6NW0y5bJ+U/CG8DIEACvbUlZpsQAQM51WGpcrLxcz8QMQPFckwkZOT3/VF0XaWk5Fem2AA7fIyIiIiIiIiKiEsCkFBERERERERERqR2TUkREREREREREpHZMShERERERERERkdoxKUVERERERERERGrHpBQREREREREREakdk1JERERERERERKR2TEoREREREREREZHaMSlFRERERERERERqx6QUEVExypZmY+2WAPgNa4seI9rDb1hbLFg+DckpSWqPJXDTcmRlZRaq7t2Ht9FrZAf4//AtLt88p/SciKio+fjMxqNHMYWq+/x5PGrW/KmYIyL6MM9X9EPkbz0gSLPlZenPbuPZgrZ4cyKwBCMj+nCnLh5Fvx87o8/oTug+vB1G/tQPMpks33ViXkahda96xRpXckoSNu4s/Pvq7OUT6D68HfqM7oQn4Y+UnpP6MSmVj8jISPTv3x+2trbQ1taGk5MTRo0ahbi4OIV6oaGh6Nu3L+zt7aGjo4MyZcrAz88P165dU6h38uRJtG7dGhYWFtDX10elSpUwZswYREVFAQCCg4NhamqqMhZTU1MEBwfLn4tEIvnDyMgI1atXx86dOxXWSU9Ph5mZGczNzZGeni7fxtvrqnqcOnVKZSzp6emYNm0aypcvDx0dHVhaWuK7777DvXv3FOpNnz4dIpEIgwcPVii/desWRCIRwsPD8zvs8vXzezRt2hSenp7IzFT8gn3w4EFoaWnh2rVrCA8PV1jHzMwMDRs2xOnTp+X1/f39VbbfsmXLfGMkKqx5v/+EkMd3sWrBRmz8bS82/rYXNbzrIiklsdBtyGSyAv/oF8baLQHIys4qVN2/T+5By8btEbxkB2pVqa/0nIjoSyCVFv6zNTtbWoyR0JdG07gU0kIvy5+n/nMU2tZuJRhRwQQZz3FSLe7Na/xv5UzMnbgM65buxF+/78Mw/7EQiUQlHRpSUpOxcdfaQtfffXgLvvcbjnVLd8LFuZzSc1I/zZIO4FMVFhaGOnXqoFy5cti0aRPKlCmDe/fuYdy4cfj7779x6dIlmJub49q1a2jatCk8PDywatUqVKhQAcnJydizZw/GjBkjT4CsWrUKQ4cORZ8+fbBjxw44OzsjIiIC69evx6JFi7B48eL3jjEoKAgtW7ZEQkIC/ve//6Fz5844d+4c6tSpAwDYsWMHPDw8IAgCdu7ciR49eqBr164KCZdOnTrBw8MDM2fOlJeZm5srJY4yMjLQrFkzREREYNGiRahVqxZevnyJefPmoVatWjh27Bhq164tr6+rq4vAwED8+OOPKFfu/d7cY8eOVUho1ahRAwMHDsSAAQPkZXp6evDy8sK0adMwb948AEBCQgIGDhyIKVOmoHr16vJ9OHbsGNzd3fHq1StMnjwZrVu3xt27d1GmTBkAQMuWLREUFKQQg46OznvFTKTK85hnOHn+CHb+cQzGhiYAALFYjCb1fOV1Nu4MxMGTeyAWieHiXA5jB/0EQwMjBG5ajqgXkUjPSEdUTAQW/bwSYRGPEbx1JTIyMqChoYFh/mPh7V5dabvBW1fiyJkD0NLUAgAsmPw71u9YAwAYPLEnRCIRlk5fg6u3L2Lb/j+RlZ0FQRAwqOdo1K3eEBt2rMHxc39DV0cPR07vR7MGrRSe/z47GEaGxmo4gkT0tZs/fx+uXHmCrCwpjIx0MXduFzg7l3pr+V5cvfoUaWkZ+PnnjqhTJ+dL/+7d17BmzUmIRCJYW5ti9uzOsLY2wY4dV3DgwC1YWBgiNPQlfv65IzQ0xPjf/w4gJUUCmUzAkCHN0LJlZTx/Ho9OnZagV68GuHDhEVq08ISjoyWWLPkbIpEIUqkMP/7YCs2aeZTU4aFPmKFnc6T+cxQG5etBlpGKjOiHMKjYEEJ2JjJfhyP+SACErAwI2ZkwcG8MkzpdAACxB5ZApKWD7PgoZCe9hlYpJ5RqPx4iDS2kh99CwtkNELKzAFk2jGt0hGHl5gCAzNgIxB1cCiFLAq1SZZCd8AImdbtC37UmpClvEH9sFbKTXkLIzoK+W22YNugJIKdXl5GXL9Kf3YKGoTlM63VH3MGlkGWmA4IAPbfaMGvYq8SOI30a4t7EQkOsAWMjE3lZeZdK8v//HvwLbt69guzsbBgaGGHisJlwsHVSaufB4zsIWL8YqWmpEAQZen83EI3rtsCbxHjMWDwBcW9eQyQSobxLJUwZOUdhXZlMhiV/zMW125egraUFDbEmVsz/E/9bOQMpqcnoM7oTNDQ0sXbRVmzeE4yjZw9CKpVCU1MTPwyYAvdylbFkzVzcvn8dEVHh2LJ3PSq4uis8X7VgY/EdRMoTk1J5GDZsGLS1tXHkyBHo6ekBABwdHVGlShW4uLhgypQpCAgIgL+/P9zc3HD27FmIxf91PPP29saoUaMAAM+fP8fIkSMxcuRILFmyRF7H2dkZDRs2REJCwgfFaGpqCmtra1hbW2PlypXYvHkz9u7dK09KBQYGomfPnhAEAYGBgejRowf09PTk+wMA2tra0NfXh7W1db7bWrp0KS5evIibN2/Cy8sLAODk5IQdO3agVq1a6N+/P+7evSvPlpcvXx5WVlaYOnUqtm7d+l77ZWhoCENDQ/lzDQ0NGBkZKcUYHByMFi1aoEOHDqhVqxZGjx4NGxsbTJ06VaGehYWF/DitWrUK9vb2OHLkCAYNGgQgJwFV0P4TfYiHTx7AwdYRpsZmKpdfvH4WB07sxqr5G2FkaIwFy6dh1Z9LMWZQzpCUG3evIGjRNpiZWiDqRSTWbl6BxdNWwUDfEM9jnmHYFH/sWH0Emv8mnwAgKSURm3YHY2/QKejo6EKSkQ6RSIzxQ6Zhz+GtWDn/T+jrGQAAalWpj+YN20AkEiHmZRQGTeyBnWuOote3A/Ds+VNUcHXHd216AIDScyIidRg4sDEmTmwHANi//ybmzNmDNWu+BwC8eZOGcuVsMHFie9y8+QxDhwbh+PFJeP48HgsW7MeuXT/A2toEAQHHMHXqNvzxR856168/xZ49P8LZuRSSktLRq9cKrFnzPaysjBEfn4KOHZeiWjVn+TacnCwxYkQLAEC7dr9g5sxvUbVqGchkMqSkZKj/oNBnQcfBHUnX9yE7ORbpoVegX6Ee8O93BU1jK5TuOgciTS3IsjLw4s9x0HWuAh2bnKRq1sswWHWbA5GGJl7+NRFpDy/AoFIjaFu7wrrHQojEGpCmJyMmeBR0y1aDpqE54vYvglGNDjB0b4yMF6F4sf5HeSyxBxbDpG5X6Dp4QJBJ8Wr7DKQ9ugD9cnUBANlJr1G621yIRCLEH1sFPZca8iSZND1ZzUeOPkWuzuXhUd4LnQY0QxX3GvCo4I0WDduglEVpAEDPTv0x3H8sAODY2YP4de0C/G9qgEIbySlJWLhiBv43dQUszUshIekN+o3pjMoVq+Do2YOwsbLF0hk5P6ImJScoxRAa/hDX/7mMjb/thVgsRkpqMrQ0tTBu8DT0H9sF65b+N2rI16c9un3jDyBnSop5v/+EP3/dgx8GTMaT8Efw6+CPejV8AEDpOakfk1IqxMfH4/Dhw5gzZ45CAgcArK2t0aNHD2zZsgUDBw7EvXv38NdffykkpHLlDn/btm0bMjMzMX78eJXby2vI3vvQ0tKCpqYmsrJyhuY8efIEFy9exM6dOyEIAkaPHo2wsDCULVv2g9r/66+/0Lx5c3lCKpdYLMYPP/yAHj164Pbt2/D29pYvmz9/PmrUqIGrV6+iRo0aH7xvefHx8ZH3Pps1axa2bt2K69evQ1Mz79NaX18fAOTH6UNkZGQgI+O/i9CkJPXPDURfhmu3L6JFwzbyXkcdW3XDz7+MlS+vW70RzEwtAACXb5zD8xcRGDq5t0IbL2NfwM7aQf7cQM8Q9raOmLFkAmpWqYe61RrCylJ10jX61XPMWDIer+JeQkOsgcTkN3jxOhr2Nsq/bBERlYRz5x5hw4ZzSE3NgEwmICVFIl+mpaWBDh2qAQCqVHGCpaURQkKicffuczRuXBHW1jm/6PfoURcBAUchCAIAoFq1MvLeVjduhCMyMg79+6+RtysIAsLCXsPOzgw6Oppo166KfFmdOm6YM2cPfH0ro3798qhUya7YjwF9vgw9miD17gmkPb4Iy7ZjkXr/FABAyM5E/NEVyHwZBohEkCbHIvNVmDwppVeuLsRaOb32tW3KISshZ641WXoS4v5ehuz4KECsAVl6ErJiIyDW0kVm7DMYVGoEANCxdoVWKeecdTIlkET8A2lagjwuIVOCrLjn/8Xp2Uz+w7KOgwcSTq6FLEsCXQcP6Dp7F+MRos+FWCzG3InL8Ox5GG7eu4ZL189i/bbVCFy0BfY2Trhy8zy2H/wLaempEGQypKanKrVxJ+QWol88x5iZg/4rFARERIXDo5wXtu5dj9/WLoS3R3WVU0XYlrZHdnYW5v42FVU9a6Ju9UYqv4MDwKOwB1i/fTUSkxOgIdZAeOQTZGVlQktLu8iOCRUdJqVUePz4MQRBQMWKFVUur1ixIt68eYPHjx8DACpUqFBge8bGxrCxsSnyWIGcJMn//vc/JCUloWnTpgCAtWvXolWrVjAzy+mh0bJlS6xduxazZ8/+oG08evQIjRs3Vrks9zg9evRIISlVtWpVdOnSBRMnTsTx48c/aLsFmTdvHg4fPoxu3bph0aJFeb5mAJCamopJkyZBQ0MDjRo1kpfv379foWcWAEyYMAE//aR6AtV58+ZhxowZRbMD9EUr71IRkdERSExKgImxqdJyAYLSWPy3n+vr6ivUrV2lPn4aPS/fbWpoaGD1gk24E3ILN+9ewcAJ3TH9x//B272aUt1pv4zFcP9xaFg753OjZc86SvO0ERGVlOjoN5g9exe2bx8NR0cLhIREo3fvFfmuIxKJIAiKn6Xvfs7q6/83RF8QBJQvb4tNm4YptfX8eTz09bUV1p88+Rs8fvwCly6FYsKETWjXrioGDmzyobtIXzgDj6aIWTcKWmZ20DL/L4GZcGY9NPRNYdP3V4jEGni1aw6E7P/+/ore6gEtEomBf+d6ij+8HHquNWHUYXJOL+fgUf+uJwAQ/ft4hyADIIJN7yUQaaj+6ifS1v0v5vL1oGNXEZLwm0i+sR9J1/agdGde91IOJ/uycLIviw6+XfDjjIE4d+UkfOq2wNI/5mHN/zbDztoBoeEPMfKnfirWFuDiXA4Bc9erbDt4yQ5cvX0Jpy4exZqNvyFo8XZoaGjIlxsaGOHP3/bi5t2ruHH3ClZuWIqAueugIVY8r7OyMjFlwWj8PjsYFVzdkZqWghbdayErO4tJqU8UJzr/ALm/tOX+W9AEb4Kg/MWzKPj5+cHQ0BD6+vpYvHgxfvnlF7Rq1QpSqRTr1q1Dz5495XV79uyJdevWQSot+gkM8zsOs2fPxtmzZ3HkyJEi3y6QM7fUmDFjoK+vLx8u+a66devC0NAQRkZG2LdvH4KDg+Hp6Slf3rhxY9y6dUvhMWyY8sVprkmTJiExMVH+iIyMLPL9oi+DvY0TfOo0x7zff5LfbU8QBPx9cg+ex0SghlddHDv7t/zXpD1HtqG6V22VbdX0rodLN88h7Nljedn9R/8o1UtNT0V8Yhy83auhb9chqFyxKh4/fQAA0NczQGpairxucmoSrK1sAQCHT+0rkTsCEhHlJTlZAi0tTZQqZQRBEPDnn+cVlmdlSbF37w0AwO3bEYiNTUb58jaoW9cNp08/wOvXOZ9pmzZdQJ06biqvU6pWdcazZ69x8eJbn633o5CZma1UFwCePHkJNzdr9OpVH35+dXHrVkRR7S59gTSNLGDWsA/MfPwVymWSFGgYWUIk1kBW3HNIwm8Vqj2ZJAWaxlYQiUSQRN5F5qunAACxjgG0LR2R9iBnLtvMl0+Q9Tr832X60HWohMRL2+TtZCfHITspVuU2suKjoKFvAkOPpjDz6YvM6Ifvt9P0RXod9xL/PLghf56Ukojol1Gws3ZEamoKNDW1YGFmCUEQsOPAXyrb8Kjgjecxz3D9n0vyskdhD5CVlYnol8+hp6uPpvVb4scBUxAZHY50SZrC+m8S4yGRpKFWlXoY3HM0bKzs8DTyCQz0DSDJkCD737tdZmZlQirNRul/RwpsP8B5oj517CmlgqurK0QiEe7fv48OHTooLQ8JCYGZmZl8Au8HDx4o9BB6V7ly5ZCYmIiYmJh8e0sZGxsjJSUFUqlUISsslUqRkpICExMThfpLlixBs2bNYGxsDCsrK3n54cOHERUVha5duyrUl0qlOHLkCFq1apXf7ue5D/fv31e5LCQkBADg5qZ8RxEXFxcMGDAAEydORGBg8dwCV1NTExoaGnkm/rZs2YJKlSrB1NQUFhYWSssNDAzg6upa6O3p6OhwInQqtMkjZiF42yoMGO+X874WBHi5V0f9Go1hX80RYc8eYdCE7hBBJJ/oXBUHWyf8PHo+5i3/GZmZEmRlZ6Nc2YqY/uNChXqpqcmYsvAHSCTpEIlEsLdxRKvG3wAA/L7xx4if+kFHWwdLp6/B6P6TMHn+KJSysIJ7eW+ULlU8vTmJiAqrT59V0ND47zfThg0roHXr/8HGxhT16ineOMXMTB/PnsXi22+XIS0tA4sX94C+vg7c3KwxZkwb9O27GgDkE52rYmKij1Wr+mPBgn2YO3cPsrJksLU1xYoVfVXWX7ToIJ4+fQ0tLQ3o6Wljxoxvi2jP6UuVOxH520zqdkXs/kVIvX8KmiZW0HWsXKi2TBv5I/5oABIvbYeWpQN0bP57T1i0+QFxB5ch6couaJd2gbZVGYh1cnpcW7Ydh/gTfyA6MOdHV5G2Lix8hwHGlkrbSHt4Hqn3TgEamgAEmLfI+4da+npIpVIEbVmBmFdR0NHWhVQmRavG36BBrZyeok3q+aLniG9QupQNanjVUdmGsaEJFkxZjuXBv+DXtQuQnZ2N0qVsMG/Sb7h59yo271kHsVgMmUyGoX3GwtDASGH9V7EvsGD5NGRLsyETZPAs7406VRtAU1MLLRq1Qe9RHaGro4e1i7bie7/h+H5cN5QuZYP6NVWP9qFPh0jI7eZCCnx9fXHv3j08fvxYYV6pFy9ewMXFBb1790ZAQAA8PDygp6eHK1euKI1pTUhIgKmpKSIjI+Hq6oqhQ4cqTHT+br379+/D3d0dV69eRfXq/91R6+rVq6hZsybu3buHSpVy7nIgEomwa9culUmzb7/9Ftra2pgyZYpC+fz58yGRSLB9+3Z5mY+PD7y9vbF06VKFusHBwRg9erR8EvZ58+ZhypQpChOdAzl3QahVqxbS0tLkE51Pnz4du3fvxq1btwAAL1++hIuLC4YNG4aFCxfi6dOncHZ2zvPYv8vZ2RmjR4/G6NGjVS5/N9Zc4eHhKFOmDG7evJln0tDf3x8JCQnYvXt3oeN5V1JSEkxMTJB4fAGMZW8+uJ0S4eQDuPliR/Q6xGa+KpIm65k3hYdxVWyZcwWxkSkFr/AB3GpYoUV/D9w+FYbUREnBK9BXx9LOGOWq22PDxFF49fRJSYfzWatQrxHajByHW7u3QM/EDOUbt8DLX28gK1p5vojPjUn7sjCqa4cXL17A2toakZF/IiOjaD4Liaj4GRpWgLV1azS/+hB3UtIVlo10tMJkF1u0+fUs7kV/+T2BZZkSiLR0IBKJkBkbgZebJsF2wCpo6BoWvPInaKhPWYxvWRFY1RCIuV3S4eTvu7WAx7d42ulbSPL4EV8V47ZtYPfLL8CObYCDA1CzNq9tSYmdmwWcKpXGrd1bkBr3Xw9Hy7JuxXZNZtHPHXrlzFVeF2lp1YCzc0MkJibC2Lho7sbNnlJ5+P3331G3bl34+vpi9uzZKFOmDO7du4dx48bBzs4Oc+bMgUgkQlBQEJo1a4aGDRti8uTJqFChAlJSUrBv3z4cOXIEp0+fhoODA5YsWYLhw4cjKSkJvXv3hrOzM54/f47169fD0NAQixYtQqVKldCqVSv069cPixcvhouLC548eYIff/wRrVq1kiek8vP69Wvs27cPe/fuhYeH4i2K+/TpgzZt2uD169coVapUHi2o9sMPP2DPnj1o164dFi1ahFq1auHly5eYO3cuHjx4gGPHjuXZU6l06dL48ccf8b///e+9tqkuGRkZePHihUKZpqYmLC2Vfz0iIiIiIvrUZETdx5uTa+XPLVqO+GwTUkT0deGcUnlwc3PDtWvX4OLigq5du8LFxQUDBw5E48aNcfHiRZibmwMAatasKa83YMAAVKxYEe3bt8e9e/cUeh8NHToUR44cQVRUFDp27IgKFSrg+++/h7GxMcaO/e9uW5s3b0azZs0wZMgQVKpUCUOGDEHTpk2xadOmQsW9fv16GBgYyCc8f1vjxo1hZGSEDRs2vPfx0NXVxYkTJ9CnTx9MnjwZrq6uaNmyJTQ0NHDp0iXUrq16Hpxc48aNU5pM/FNx6NAh2NjYKDzq11e+4wMRERER0adIr0xV2Pb7Xf7Qd8v/2pyI6FPBnlL5cHJyQlBQUIH1ypUrh3Xr1hVYr1mzZmjWrFm+dYyNjbF48WIsXrw433p5jbocM2YMxowZo3KZpqYm4uLiFMpOnTqlsq6/vz/8/f0VyvT19TFr1izMmjUr39imT5+O6dOnK5QZGRnh9evX+a6Xl/Dw8HyXq4oVyBn2V9Do1ODgYAQHB39QXERERERERET04dhTioiIiIiIiIiI1I5JKSoRgwcPhqGhocrH4MGDSzo8IiIiIiIiIipmHL5HJWLmzJkKc2m9rahm8SciIiIiIiKiTxeTUlQirKysYGVlVdJhEBEREREREVEJ4fA9IiIiIiIiIiJSOyaliIiIiIiIiIhI7ZiUIiIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjvefY+oqOiXAkR6JR3F+9E1BgCYalkUXZPinGNgbmNQZG2+y9gyZxt6RtrFtg36vOnoawEAzO0cSjiSz5+xVWkAgL6pOXQMjQAAmlb6JRlSkREb5JwnWlq5/5qXZDhE9J40NXOuY9wMdJWWWevkvK9drQzVGhMVjdLG/76mluVKNpDC0DUFAGiXLfteq2nZ2ef8x9QM0M+5bua1Lb1LWzcnZaNvqniNomuU8/lXHNdkIh0NAKqvi0TF8H1XJAiCUOStEn1FkpKSYGJigsTERBgbG5d0OO9NJsggFhVtp0mZTIBYLCrSNt8lCAJEouLdBn3eZDIZxGJ2CC4KgkwG0b/HUpAJEBXz+1udcj9LBEEGURF/FhJR8ZMKAjTyuB6QygRofEGfV18bQSaFSKxR0mEUiiCVQqTx/rEq/H3ltS3l4e3zRLG8+K7J8rouKo7vvrz6IvrKFXVCCkCxJ6QA8I82FYgJqaLz9oXQl5SQAv77LGFCiujzlFdCCgATUp+5zyUhBeCDElLAO39feW1LeVCVkMopL75zRp3XRbwCIyIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjsmpYiIiIiIiIiISO2YlCIiIiIiIiIiIrVjUoqIiIiIiIiIiNSOSSkiIiIiIiIiIlI7JqWIiIhKiCAIJR3CV0MQpJDKeLyJiIiIPiWaJR0A0Rfj1EkgK7OkoyCiz0X7DhBpaeHx48dIT08v6Wi+aKampnB0dISGCBi1+SZCX6WUdEhEREREnx0Hw6Jvk0kpoqKSmACkpZV0FET0mUlPT0caPzuKlZ6envz/oa9ScC86qQSjISIiIvo8ZZlrFHmbHL5HRERERERERERqx6QUERERERERERGpHZNSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqR2TUkREREREREREpHZMShERERERERERkdoxKUVERERERERERGr3VSSl/P39IRKJMH/+fIXy3bt3QyQSKdUvX748tLW1ERUVpbTMx8dHZVsA0Lp1a4hEIkyfPl2p/ruPwYMHFyp2kUiE3bt3KzzX1dXFs2fPFOp16NAB/v7+CmUvXrzAiBEjULZsWejo6MDBwQHt2rXD8ePHFepduHABrVu3hpmZGXR1deHp6YlFixZBKpUqxSISiXDp0iWF8oyMDFhYWEAkEuHUqVNK9d99bN68Od99zn298ns0a9YMvr6+SusGBATAxMQEEREROHXqlMI6pUqVQqtWrXD79m15/Y99fej9ZUulmHngICpMmwH3GbNQYdoMDPxzIxLS0ko6tA/289592HLtWoH1fBYtwf5/7qghovfjPXsu0jMz862TkJaGhYePqCkiIiKiT1fCuY2I+/tX+XNJxD94tqAtJM/vy8tiDy5DwvlNhW5TEvEPYtaNVrks7u9fIYm8+8HxEhF9yr6KpBQA6OrqYsGCBXjz5k2+9c6dOweJRILOnTsjODhYZR0HBwcEBQUplEVHR+PEiROwsbFRqj9gwADExMQoPBYuXPjB+yISifDzzz/nWyc8PBzVqlXDiRMnsHDhQty5cweHDh1C48aNMWzYMHm9Xbt2oVGjRrC3t8fJkycREhKCUaNGYc6cOejWrRsEQShw33ft2gVDQ0OVcQQFBSnte4cOHfKNfdmyZQr1VbUTFBSEy5cvY9WqVfL1nj59igkTJmDZsmVwdHSUlz98+BAxMTE4cOAA3rx5g5YtWyIxMVG+vKhfH8pf/w1/4mp4OC6OH4d7037C/Wk/oXnFiohP/bSTUtnvJGnfNrN9O3StXl2N0RStW1MnQ09bO986CWnpWHjkqJoiIiIi+nTpOlaGJOK/H5kkEXegbVMekoh/5GUZkXeg6+hZqPYEWd7XGABg0WokdB08PixYIqJPnGZJB6AuzZo1Q2hoKObNm5dvwiEwMBDdu3dHo0aNMGzYMEyePFmpN1Xbtm2xdetWnD9/HvXq1QMABAcHo0WLFoiIiFBqU19fH9bW1kW2LyNGjMCiRYswduxYeHqq/mM3dOhQiEQiXLlyBQYGBvJyd3d39OvXDwCQmpqKAQMGoH379li9erW8zvfff4/SpUujffv22Lp1K7p27Spf1qdPH/z6669YunQp9PT0AABr165Fnz59MGvWLKU4TE1N33vfTUxMYGJiUmA7y5Ytw/Dhw9GiRQs4Ozujf//+aNq0qVKPMSsrK/n6ixYtQv369XHp0iV5T6uifn0ob6GvXmHb9RuImDcHZgb6AACxWIzO1arK6yw8fATrLl2GWCRCZTs7BHTvBhM9PUzftx8PX75CcoYEIS9eoqqDAya19MWYHTsQHheHDl5eWNz5OwA5PZJqODvh8tNwPI2LxcjGjeFgZoZfT55EVEIiFnTsgG41cpJIV8PDMWHXbiSlSyATBExp1RLfVq2C8Ng4VJ83HyObNMbRBw/QydsbXapVw6it2/Do1UsAwDdeXpjVvh38g9ejupMjhjf2wfGQEEzdsw+S7CxkSaUY06wZ+tatk+9xyZZK0WZ5AOJSUpGelQVve3us6dUD+trauBT2FMM2b4ZUJiBbJsWwRo0wpFFD/HHuPBYfOw5tTU1IZTL80asHapUpg2vPnmHE5q1IzcyArqYWlnT+DvVcXQAAB+7cwfT9B5CZLYVIJMKqHn6oVaYMRIOHInnpYhjq6mLcjp049egRsqQymOjp4o+ePeFW2gqD/9qEhPR0eM+eC02xGNcmT8SLxESM3LIN4XFxkGRnoYOXF2a2b1fUpw0REdEnRce2AqQp8chOioWmsSUkEXdgWq8bkq7uBup2Q3bSa0hT3kDL3A6vds5G9psYAAKMqrWHkXdLAMDzFf1g5OWL9Ge3oGFoDiOv/0YAyDJS8XrXPOg4uMO0nh9e/DURxjU7Qd+1JmIPLIFISwfZ8VHITnoNrVJOKNV+PEQaWpBlpCLu4DJkxkZAw9AcmkYW0NA3hVmT/iVzoIiICuGrSUppaGhg7ty56N69O0aOHAl7e3ulOsnJydi2bRsuX76MChUqIDU1FadOnULjxo0V6mlra6NHjx4ICgpSSEotXLhQYehecalbty4ePnyISZMmYf/+/UrL4+PjcejQIcyZM0chIZXL1NQUAHDkyBHExcVh7NixSnXatWuHcuXKYdOmTQpJqWrVqqFMmTLYsWMHevbsicjISJw5cwbLly9XmZQqTn369MGuXbvQt29ffPvtt7h79y7u3s2/a3NuIi0rK0sdIdI7bkREws3KCpZ59Kz7++49BF28iIvjx8FUXx8D/9yIybv3YLlfNwDAtWfPcG3yRBjq6KDqnHmYuHs3/h4+DNkyGcpM/QmDGzZAudKlAQAR8fE49eNovEhKgstP0zCmWVNcGD8OV56Go8PKVehWozoS0tIwaOMmHBg+FDYmJohNSUG1ufNRz6UsACAuNRWupUrh5zatAQCNFy9Baw8PbB80AADwOjlZaR+qOjji3Lgx0BCLEZ+aiqpz5qGleyXYvJNofZuGWIy/+vWFhaEhBEHA0E2bEXDqNMa2aI55hw5jTLNm6F6zBgDgzb89ysZs34EH03+GrakpsqRSZGRlITM7G51Wrsaanj3g614J50JD8d3qNQidNQNRCQnov2Ejzoz5AeVKl0aWVIo0FUP2Jvi2wP++7QQA2Hz1Gn7Yvh37hw3Fyu5+qD5vPm5NnSyv22fdekxp1RIN3dyQLZWi7fIV2HXzFjpW8c77JCAiIvrMiTS1oGOX0zPKoEIDSJPjoOdSA/HHV0OQZkES8Q907Coi/thqaFnYw6rTVEhTExCzbjS0rcpAx7Y8ACA76TVKd5sLkUgk72WVnfQKr3fOgVGNDjB0b6xy+1kvw2DVbQ5EGpp4+ddEpD28AINKjZBwfhPEuoawG7ASUkkKYoJHwaBcXbUdFyKiD/HVJKUAoGPHjvD29sa0adMQGBiotHzz5s1wc3ODu7s7AKBbt24IDAxUSkoBQP/+/VG/fn0sW7YM169fR2JiItq0aaMyKRUQEIA//vhDoWz58uXo06fPB+/LvHnzULlyZZw9exYNGjRQWBYaGgpBEFChQoV823j06BEAoGLFiiqXV6hQQV7nbX379sXatWvRs2dPBAUFoXXr1ihVqpTKNvz8/KChoaFQ9s8//6Bs2bL5xlZYq1evhoeHB86ePYvt27fDysoqz7pxcXGYMWMGjIyMULNmTXn5+74+GRkZyMjIkD9PSkr6yL2gXMdCQtCjZk2Y6uf0ohrSsCG6vfVe9a1UCSb/JhYr29vBy84OOlpa0AFQvnRphMXGypNSnatWhVgshq2pKSwNDdDB2wsAUM3JETGJiZBkZeHCkzCExcai1W/L5dsQBAEPX76Ek7kFdLW04Pdvj6oUiQQXwp7i6KiR8rqljIyU9iEuNQX9N2zAo1evoCnWQGxqKu5Fx+SblBIEAUuOn8CBu3eRLZUhMT0dDd1cAQCNy5fD7IN/I/T1azQpXw71XXPKm5Qvj97B69DO0xOtPNxRrnRp3ImKgramJnzdKwEA6ru6wsrICP9EReFGRCRa/1sPALQ0NOTH8m1H7j/AbydPITlDAplMQJJEojLm1IwMnAh5iJdJ/yXmUjIyEPLyRZ77SURE9KXQ+XcIn6axJbRtywEAtK3LISP6ISQRd6DrVBlJV3bBpu9vAAANA1Pol6sDybPb8qSUoWczhREZ0pR4vPhrEixbj4KuY+U8t61Xri7EWjo527Qph6yEnOkuMiLuwKzZoJzt6RpC36120e84EVER+6qSUgCwYMECNGnSBGPGjFFaFhgYiJ49e8qf9+zZEw0bNkRCQoK8d1GuypUrw83NDdu3b8fJkyfRq1cvaGlpqdxmjx49MGXKFIWy/JInhVGpUiX07t0bEyZMwIULFxSW5c4DpWoSd1XenTfq7XJVbfTs2RMTJ05EWFgYgoOD8euvv6pYO8eSJUvQrFkzhTIHB4dCxVUYVlZWGDhwIHbv3o2OHTuqrJPbKy41NRVubm7Ytm2bwvF/39dn3rx5mDFjRhFE//Wp6uiAx69eIS4lBRYqeksJgoB3zzjRWyW6Wv99ZGmIxNB96z2nIRIjWyp7q67iMl3NnOca4pyp9LKlUggQUNnODmfG/qgUS3hsHAy0tQv9Pso1+K9NaOfpiR2DBkIkEqHqnHmQFNAz76+rV3H68WOcGfMjjHR18euJkzjzOBQAMLppE7SvXBnHQ0IwefdeeNjaIKC7H3YOHojrERE49fARWv8egNnt26GSjbXS8QMUj2F+IuLjMXLLVlyZOAFlS1nin+fP0WTJMpV1Zf9+PlydNAFa7ySeiYiIvnS6jpUR988iaBpbQtfB898yD0gi7kAScQdGXr5IurILyhc2/xWItHUVFol1DaFpbIX00Kv5JqVEmv9d44hEYuDfOalyrunf77qFiKikfTUTnedq2LAhfH19MXnyZIXy+/fv4/Llyxg/fjw0NTWhqamJ2rVrIz09HZs2qb5zRr9+/bB8+XJs375dPk+TKiYmJnB1dVV4GBsbf/S+zJgxAzdv3lS4Ox8AuLm5QSQS4cGDB/muX65czq86edULCQmBm5ubUrmFhQXatm2L/v37QyKRoFWrVnluw9raWmnf80refajc1ysvZ8+exe3bt5GYmIhHjx4p3bXvfV+fSZMmITExUf6IjIwssn350rlaWeHbqlXQf8Of8rvtCYKA9Zcu4cnr12hesSI2X7uO5H9756w+dw7NCujx9zHqlnXB41evcCLkobzsVmQkMrOzleoa6uqivosLlhw/IS9TNXzvTVoanCzMIRKJcObxY9x+/rzAON6kpcHCwABGurpIlkgQfPG/O1w+fPESZUtZYkCD+pjcyheXnoYjWyrFk9evUd3JCWNbNMd3VavgSng4KlhbIyM7W74/F548wavkZHja2cK3UkX8ffceHr3MmQ8rSypFYnq6QhyJ6enQ1tSEtYkxBEHA76dOy5cZ6+kiLTNTPuG7ka4uGri6Yv6hw/I60QkJeF7AzSSIiIi+BDo25SBNS0Tq/dPyCc11HTyRev8UZGmJ0LZ2g66zN1Ju5fydlKYlIu3RpQKSTdoo1WkqspNeIe7w7xAEWZ51VdF19ETq3Zy7bMskKUh/fKmANYiISt5X11MKAObPnw9vb295UgbI6SXVsGFDLF++XKHuhg0bEBgYiCFDhii10717d4wdOxZeXl6oVKlSscf9LgcHBwwfPhyTJ0+Gi4uLvNzc3By+vr5Yvnw5Ro4cqTSvVG7PrxYtWsDc3ByLFi1C3bqK48337t2Lx48f5zlPVL9+/dC6dWtMmDBBaXjep6ZMmTJKPd0+ho6ODnR0dIqsva/N2t69MPvg36i1YCE0xRoQBAEN3VzRvnJltPJwx52oKNRZ+D+I8N9E58XFzEAf+4YNwbgdO/HDtu3IkkrhaG6O3UMGqay/oW8fjNiyFe4zZkFTLEYHby/MaNdWoc78Dh0wdNNmzD98BJVsbFCrjHOBcfSuXRt7bv+DStNnws7UFA1cXRCVkHOHyN9OnsLJR4+grakBDZEYi77rBKlMhr7rN+BNWho0xRooZWiIoD69oK2piR2DBmDklm1I3ZYz0fm2gd/DQEcHrlZWCOzVE36Ba5EllUJDLMaq7t1R8634PO3s0LlqFbjPmAVHczM0f2tor7mBAXrUrAnPWbNhoK2Da5MnYmM/f/y4fQc8Z84GABjq6GBlDz/Ym5m95ytBRET0eRFpaELHriKyXodDy9wOAKBlYQ8hIw069pUg0tCEebOBiDu8HNFrhwOCDCZ1u8iH7uXXrmX78Yj7+zfE7V8MizY/FDom03p+iD24FNF/DIGGiVVOHDr6H7WfRETFTSTkNXbrC+Lv74+EhASFHkW9e/fGtm3bIJFIkJmZCTs7O8ycORODBw9WWPfx48coV64cbt26BS8vL/j4+MDb2xtLly4FkJPg0dLSkid+vL290aFDB/ncUj4+PihXrhxmzpyp0K6Ojg7MCvHFTSQSYdeuXejQoYPK5/Hx8ShbtiwyMjLQtWtXBAcHAwCePn2KunXrwtzcHDNnzkTlypWRnZ2No0ePYsWKFfLeUdu3b0e3bt3Qr18/DB8+HMbGxjh+/DjGjRuHpk2bYuvWrfLhS29vWxAExMXFwdjYGNra2khISICZmRlOnjwJHx8fef2goCC0bNlSYZ+MjIxUTsBe2GPwrunTp2P37t24deuWQnnuJPVv3rzJMyn1sa8PkDOnlImJCRLXB8P4394/REQF6vc9oKWFf/75B2n87ChWFhYW8p6/bX49i3vRnAuQiL48gjQbEGQQaWpDlpGGFxvHw6zJ99Bz9i7p0IjoC1HOXANHJ7RCYmJikYz+Ar7C4Xu5Zs2aJZ9Lae/evYiLi1M5J5Gbmxs8PT1VTowO5NzJrqAEy5o1a2BjY6Pw8PPz+/idQE6vqAkTJkDyzmTEZcqUwY0bN9C4cWOMGTMGHh4eaN68OY4fP44VK1bI63333Xc4efIkIiMj0bBhQ5QvXx6LFy/GlClTsHnz5jzn0xGJRLC0tIS2tna+8fXt21dp33/77beP3/EiVJyvDxERERGROsgkKXjx5zhEB41AzPofoF++HhNSRPTJ+yp6ShEVJ/aUIqIPwp5SasOeUkREREQfjz2liIiIiIiIiIjoi8CkVAmaO3cuDA0NVT7yu6Pdl6BVq1Z57vvcuXNLOjwiIiIiIiIiKmZf5d33PhWDBw9Gly5dVC7T09NTczTq9ccffyD9ndvR5zI3N1dzNERERERERESkbkxKlSBzc/OvNgFjZ2dX0iEQERERERERUQni8D0iIiIiIiIiIlI7JqWIiIiIiIiIiEjtmJQiIiIiIiIiIiK1Y1KKiIiIiIiIiIjUjkkpIiIiIiIiIiJSO959j6iomJgC+volHQURfWb09PRKOoQvno6Ojvz/rlaGJRgJERER0efLoRguo0SCIAhF3yzR1yMpKQkmJiZITEyEsbFxSYdDRJ8RQRAgEolKOoyvgiBIIRPE0BDzeBMRERF9iOL47sueUkRERCWECSn1EYk0oMHDTURERPRJ4ZxSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqR2TUkREREREREREpHZMShERERERERERkdoxKUVERERERERERGrHpBRRMZHJZCUdAhERERER0SdFEErue5JUJs1zmUwmFMk2ZELRtPO10CzpAIi+FNFTpiIuKgoAYPvbr9Cxs8OOHTsQGxtbwpERERERERGVPD8/PxgbGwN3twCpr9S7cYvy0HBtgYlnJiIsMUxh0eiqo1HXri6Orr2H+JjUD95E+9Fe0DPQwekXb5CYmf2xEX9yTKQZRd4mk1JERSQzPBzC48cAACEzEwAQGxuLmJiYkgyLiIiIiIjokyCV/ttTKfUVkByt3o3rlwIAhCWG4UH8A4VFiZmJAID4mFTERqZ88CYEqfBve9mIy8j64HY+VZpC3j3NPhSH7xERERERERERkdoxKUVERERERERERGrHpBQREREREREREakdk1JERERERERERKR2TEoREREREREREZHaMSlFRERERERERERqx6QUERERERERERGpHZNSRERERERERESkdkxKERERERERERGR2n3WSSl/f3+IRCLMnz9foXz37t0QiURK9cuXLw9tbW1ERUUpLfPx8VHZFgC0bt0aIpEI06dPV6r/7mPw4MGFil0kEmH37t0q96lDhw4KZZGRkejfvz9sbW2hra0NJycnjBo1CnFxcQr1nJ2dsXTpUqU2ly5dCmdnZ/nz1NRUTJgwAWXLloWuri5KlSoFHx8f7N+/X2H/Ro8erbS/mzdvzrdtAMjMzMT//vc/VK1aFQYGBjAxMYGXlxemTp2K6OjofI8LAJXH9e1H9+7doa+vj7/++kthPZlMhrp166Jjx44A/js/RCIRtLS0ULZsWYwdOxapqakAgPDw8Dy3cenSpQLjJCKiL9vSpUvx6tUrhbLg4GA8evSohCL6j1QqxdKlSyGVStW+7YSEBFy/fl2t25RIJDh//rxat0lERETF77NOSgGArq4uFixYgDdv3uRb79y5c5BIJOjcuTOCg4NV1nFwcEBQUJBCWXR0NE6cOAEbGxul+gMGDEBMTIzCY+HChR+8L6qEhYWhevXqePToETZt2oTQ0FCsXLkSx48fR506dRAfH//ebQ4ePBi7d+/G77//jpCQEBw6dAjffvutUpLrXbq6upg6dSqysrLyrJORkYHmzZtj7ty58Pf3x5kzZ3D9+nUsXLgQcXFx+O233wqM7+3juXTpUhgbGyuUrVixAvPnz8eIESMQExMjX2/RokUIDQ3FqlWr5GUtW7ZETEwMwsLCMHv2bAQEBGDs2LEK2zt27JjS61itWrUC4yQiIiop4eHhcHBwgIaGhlq3K5PJmJQiIiKiIqNZ0gF8rGbNmiE0NBTz5s3LNyEUGBiI7t27o1GjRhg2bBgmT56s1Juqbdu22Lp1K86fP4969eoByPlFtEWLFoiIiFBqU19fH9bW1kW7Q+8YNmwYtLW1ceTIEejp6QEAHB0dUaVKFbi4uGDKlClYsWLFe7W5b98+LFu2DK1btwaQ08OqMEkYPz8/7Nu3D2vWrMHQoUNV1lmyZAnOnTuHa9euoUqVKvJyV1dX+Pr6QhCEArfz9jE1MTGBSCRSOs4jRozAnj17MGDAAOzfvx8hISH4+eefsWnTJlhZWcnr6ejoyNft3r07Tp48id27dyscMwsLi2J/HYmI6MuTkpKCAwcOID4+HoIgoFatWvK/p0uXLkX37t3lf5NWr16NFi1awNnZGWfOnMGdO3fkCaVu3brB1NQUUVFROHbsGDIyMiAIAho0aIBKlSqp3HZISAgqVKigVP7y5UscPHgQmZmZyM7ORuXKldGgQQMAOT3JxWIxEhISkJSUBHt7e7Rt2xaampq4c+cOLl++DKlUCkEQ0LRpU7i5ucn3pWrVqnj69CmMjIwQHR2NxMRErFy5EiYmJvDz88PSpUvh5eWFsLAwJCUloWnTpkhLS8OdO3eQnp6O9u3by3tWh4aG4syZM8jOzoZYLEbz5s3h5OSE8PBwHDp0CA4ODoiMjIRMJkOHDh1ga2uL/fv3QyKRYOXKlRCLxRg4cGCRvpZERERUMj77pJSGhgbmzp2L7t27Y+TIkbC3t1eqk5ycjG3btuHy5cuoUKECUlNTcerUKTRu3Fihnra2Nnr06IGgoCCFpNTChQsVhu6pS3x8PA4fPow5c+bIE1K5rK2t0aNHD2zZsgUBAQEqhyvmxdraGgcPHkSnTp1gZGRU6PWMjY0xefJkzJw5E3369IGBgYFSnU2bNqF58+YKCam3vU+c+RGJRAgKCoKnpyfWrFmDwMBAdO3aVWno47v09PTy7elFRET0tq1bt0JT87/Lpbd7KB86dAgWFhbo2rUrUlNTsXr1alhbW8POzi7P9tLT03HhwgWMGTMGWlpayMrKgkgkgkQiwf79+9G9e3cYGRkhLS0Nq1evhqOjIwwNDRXaEAQBT548QbNmzZTaNzU1Ra9evaCpqYmsrCysXbsWLi4usLW1BQBERUWhf//+0NTUxJYtW3D58mXUq1cPLi4u8PDwgEgkQkJCAgIDAzF69Gh54iwxMRG9e/eGSCRCeHg4jhw5opQYysrKQv/+/REVFSX/UW/AgAG4d+8ejh8/jv79++PNmzc4ffo0evbsCR0dHcTHxyM4OBijRo0CALx+/Rrt27dHmzZtcO3aNZw4cQI9e/ZE27ZtsXr16kJPk0BERESfh89++B4AdOzYEd7e3pg2bZrK5Zs3b4abmxvc3d2hoaGBbt26ITAwUGXd/v37Y+vWrUhNTcWZM2eQmJiINm3aqKwbEBAAQ0NDhce6desKHbefn5/S+hs3bpQvf/z4MQRBQMWKFVWuX7FiRbx58wavX78u9DaBnF9rL1y4AAsLC9SoUQM//PBDobvEDx06FLq6uli8eLHK5Y8ePUL58uUVyjp27Cjfv7p1675XrPlxdHTE0qVLMXjwYERHR2PZsmX51r9y5Qr++usvNG3aVKG8bt26Sq9DfnN0ZGRkICkpSeFBRERfpi5dumDw4MHyR25yB8gZYl+zZk0AgIGBASpUqICwsLB829PR0YGFhQV27dqFa9euIT09HZqamoiMjMSbN2+wceNGrFy5EuvXr4cgCIiNjVVqIzo6GpaWltDR0VFalp2djX379mHFihUIDAxEQkICXrx4IV/u7u4ObW1tiMViVKlSRR5vQkICNm7ciICAAGzevBlpaWlITEyUr1elSpUCf1jy8PAAANjY2CA7Oxvu7u7y57nTLISGhiI+Ph5BQUFYuXIltm7dCgDyv6UWFhbyY2xvb/9B0xQQERHR5+Oz7ymVa8GCBWjSpAnGjBmjtCwwMBA9e/aUP+/ZsycaNmyIhIQEmJqaKtStXLky3NzcsH37dpw8eRK9evWClpaWym326NEDU6ZMUSh7e+hYQZYsWaL0K+eECRMKPWlp7lC49+191LBhQ4SFheHSpUs4f/48Tpw4gWXLlmHGjBn46aef8l1XR0cHM2fOxPDhwzFkyBCVdd6NJyAgAKmpqfj1119x5syZ94q1IH379sVPP/2EkSNHwsTERGn5/v37YWhoiOzsbGRlZeGbb75Rmtdqy5YtSom//ObomDdvHmbMmFE0O0BERF+U3L+BYrEYMplMXp6dnS0v79+/PyIjIxEeHo4//vgD3377LQRBQOnSpdG3b98Ct/HgwQOlH4ByHT9+HAYGBhg0aBDEYjG2bNki33Z+8W7fvh0tWrSQDwlcsGCBwnra2toFxpXbo0wsFis9zz0WgiDA1dVVflOStyUmJir0Snv3GBIREdGX54voKQXkJFp8fX0xefJkhfL79+/j8uXLGD9+PDQ1NaGpqYnatWsjPT0dmzZtUtlWv379sHz5cmzfvh39+vXLc5smJiZwdXVVeBgbGxc6Zmtra6X13x5O5+rqCpFIhPv376tcPyQkBGZmZrC0tASQM7zu7V81cyUkJCglbLS0tNCgQQNMnDgRR44cwcyZMzFr1ixkZmYWGHfPnj3h7OyM2bNnKy1zc3NDSEiIQpmNjQ1cXV1hbm5eYNsfIvd1VaVx48a4desWHj58CIlEgp07dyolDh0cHJReh/xMmjQJiYmJ8kdkZGSR7QsREX0+ypYtK5/wOzU1FSEhIShTpgwAwNzcXH6336ioKPnNRDIyMpCamgonJyc0atQIjo6OePHiBRwcHBAfH4+nT5/K23/x4oXKH6pU9UrOJZFIYGxsDLFYjNjYWKWeW/fv30dmZiZkMhlu3bolj1cikch/qPvnn38gkUjy3G8dHR1kZGQU5hApcXFxQWhoqMJdDVXdFVnVNrOyspikIiIi+sJ8MT2lAGD+/Pnw9vZGuXLl5GWBgYFo2LAhli9frlB3w4YNCAwMVNnbp3v37hg7diy8vLzynGBUHSwsLNC8eXMEBATghx9+UJhX6sWLF9i4caN8fgcAqFChAq5evarUztWrV/O8eM1VqVIlZGdnQyKRFPhrqFgsxrx589CpUyel4+fn54epU6fi5s2bec4rpU4GBgYFJpnel46OjsohE0RE9HVp2bIlDhw4gBUrVsgnJs+dT6pJkybYvXs3bt68CWtra5QqVQpATlJq69at8vkNLSws4OXlBV1dXfj5+eHo0aM4fPgwpFIpTExM0K1bN4VtxsbGQldXV2meqVwNGjTArl27cOfOHZiamsqTTrmcnJywZcsWJCYmwt7eHrVq1ZLvy5YtW2BkZAR7e3uVvY9zlS5dGhYWFggICICZmRn8/PwKfcwsLCzQqVMn7N27F9nZ2ZBKpbCxsUGnTp3yXU9PTw+enp5YsWIFtLS0ONE5ERHRF+KLSkp5enqiR48e8uFZWVlZ2LBhA2bOnCmf5yDX999/j4ULF+L27dvw8vJSWGZmZoaYmJg8h+3lSktLU5inAchJWJiZmRXB3uT4/fffUbduXfj6+mL27NkoU6YM7t27h3HjxsHOzg5z5syR1/3xxx9Rr149zJw5E9999x0AYMeOHTh06BAuXLggr+fj4wM/Pz9Ur14dFhYWuH//PiZPnozGjRsXuqdXmzZtUKtWLaxatQqlS5eWl//www84cOAAmjRpgunTp6NBgwYwMzPDo0eP8Pfff6v91tWFERcXp/Q6mpqaQldXt4QiIiKiT8Ho0aOVyvz9/eX/NzQ0RNeuXVWua2trm+edar///vs81+nTp0++MeV1171cNjY2eW4XyEkKtWrVSqm8cuXKqFy5svx5ixYt5P9/9ziIxWJ0795doezdOm/P82lqaorx48fLn7u4uMDFxUUpBmdnZ4Vkk5WVlUK77du3V71TRERE9Nn6Yobv5Zo1a5Z8rqW9e/ciLi5O5bwFbm5u8PT0zHPCc1NTU5V3l3vbmjVrYGNjo/B4n18LC8PNzQ3Xrl2Di4sLunbtChcXFwwcOBCNGzfGxYsXFYbE1a5dG4cPH8axY8dQv3591K9fH0eOHMHhw4flv4QCgK+vL9atW4cWLVqgYsWKGDFiBHx9feWTjRbWggULlLr36+rq4vjx45g4cSKCgoJQv359VKxYEaNHj0a9evWwe/fujzoexaFZs2ZKr+OnGCcREVH9+vWL9KYhRERERCVJJORmcIjogyQlJcHExAS327aD1uPHAIAyfx+EbpkyWLVqFWJiYko4QiIiIiIiopI3atSonJFFl38DkqPVu/HSXoBnN3TZ1wUP4h8oLFrYcCFalWmFLXOuIDYy5YM30XdhXegb62JvxGvEZWR9bMSfnNJCFtqUd0ZiYuJ7zaedny+upxQREREREREREX36mJQqBnPnzoWhoaHKh6p5HL42EREReR4fQ0NDRERElHSIRERERERERFTMvqiJzj8VgwcPRpcuXVQue/sOel8rW1tb3Lp1K9/lRERERERERPRlY1KqGJibmytMQE6KNDU14erqWtJhEBEREREREVEJ4vA9IiIiIiIiIiJSOyaliIiIiIiIiIhI7ZiUIiIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjsmpYiIiIiIiIiISO00SzoAoi+FtrMzdLS0AAAibW0AgKWlZUmGRERERERE9MnQ0NDI+Y+Blfo3rmcOAChrUlZpkYm2CQDA3MbgozYh0hD9296XmWoxkMqKvE2RIAhCkbdK9BVJSkqCiYkJEhMTYWxsLC+XyWQQi9kZkYiIiIiIKJcgyCASlcz3JKlMCg2xhsplMpkAsVj00duQCQLEoo9v51OU13ffj8FvzETFhAkpIiIiIiIiRSWVkAKQZ0IKQJEkpAB8sQmp4sJvzUREREREREREpHZMShERERERERERkdoxKUVERERERERERGrHpBQREREREREREakdk1JERERERERERKR2TEoREREREREREZHaMSlFRJ8UQRBKOgQiIiIi+gJ9ydeZMpmspEOgT4z0MznfNUs6AKIvxbTz0xCVHVXSYXzW/mz5J7S1tHH1cSKS07NLOhwiIiIi+kLUq2gMHS0tZN6OhJAiKelwipS4lBG0ylljx44diI2NLelw6BPQtGlTuLq6Yuj9Z3icWnTnewdDrSJrKxeTUkRFJDwpHKGS0JIO47MmICebn5yejcQ0JqWIiIiIqGjkdhoRUiQQkr6spJRgoAMAiI2NRUxMTAlHQ58CiSTnHH+cKsGdlPQia7dO0eekOHyPiIiIiIiIiIjUj0kpIiIiIiIiIiJSOyaliIiIiIiIiIhI7ZiUIiIiIiIiIiIitWNSioiIiIiIiIiI1I5JKSIiIiIiIiIiUjsmpYiIiIiIiIiISO2YlCIiIiIiIiIiIrVjUoqIiIiIiIiIiNSOSSkiIiIiNRnUsTr8W7sjOztLXnbn2jl0qmON4F+nf3T7m1YvwLlju997vd9mjcTBbYEfvX0iIvp8PHjwAKtXr8bKlSuxfPlyrF+/HoIgAAA2btyI+Pj4j2p/9+7duHLlygete/78eZw7d+6jtv+2U6dOQSqVFll7H+rkyZO4e/fue6/3McfyU/fZJ6X8/f0hEokwf/58hfLdu3dDJBIp1S9fvjy0tbURFRWltMzHx0dlWwDQunVriEQiTJ8+Xan+u4/BgwcXKnZV69avX79Y4x09enSez2NjY2FtbY25c+cqtdelSxfUqFED2dnZ+e5TamoqJkyYgLJly0JXVxelSpWCj48P9u/fj/DwcJX7/Pbj7Xjf3fdTp04VuH5wcDCCg4NhamqqMj5TU1MEBwfLn588eRKNGzeGubk59PX14ebmhj59+hS4n0RERB+qVGk7XD17WP78+P5NcKno9V5tSFX8nZJmZ8Nv4ATUb9bhY0MkIqIvXEpKCvbv348uXbpg8ODBGDZsGJo3by5f3qNHD5ibm5dYfA8fPkSFChWKrL3Tp0+rNSklk8lUljVu3BgeHh5qi+NzoFnSARQFXV1dLFiwAIMGDYKZmVme9c6dOweJRILOnTsjODgYU6ZMUarj4OCAoKAgTJw4UV4WHR2NEydOwMbGRqn+gAEDMHPmTIUyfX39QsceFBSEli1byp9ra2sXa7z5sbS0xOrVq9G5c2e0a9cOnp6eAIDt27dj3759uHHjBjQ18z9lBg8ejCtXruD3339HpUqVEBcXhwsXLiAuLg4ODg6IiYmR1/3ll19w6NAhHDt2TF5maGiY577XrVtXYf1Ro0YhKSkJQUFB8jITExNs2bKlUPt77949tGrVCiNHjsRvv/0GPT09PH78GNu3b1f5IUJERFQUmrT1w/H9m1CncVukpiTh0d3rqN+8IzIz0vEs9AFW/zIRkvQ0ZGVK0KhlZ3zbZySAnN5MegZGiI4IQ9yrKAwYOw9rl/6MSt61EfrgFtp3G4TrF47BpYIXWnfuj+zsLGxavQB3rp1DdnYW7JxcMXj8QhgYmSDuVQx+nTkCSQlxsLJ1hOwT+PWYiIjUJzk5GWKxWOG769vfH5cuXYru3bvDysoKwcHBsLOzw/Pnz5GcnIyyZcuibdu2AICkpCTs3r0bKSkp8o4Brq6uqFmzpsL2pFIpTp48iadPn0IqlcLS0hJt27aFrq6uUmwpKSmQSCSwtLRUWrZ06VJ4e3vjyZMnSElJQZUqVdCwYUMAQHx8PPbv34/U1FSIRCL4+PigQoUK2L9/PwAgMDAQIpEIvXr1goGBgbzNhIQErF69GtWrV8fjx4+RlZWFTp064fr163j+/Dk0NTXRrVs3GBkZ4eXLlzh48CAyMzORnZ2NypUro0GDBgByOsbo6OggLi4OSUlJaN26NQ4dOgQnJydER0ejdu3aePz4MWxtbVGzZs18j0nucU1NTYWZmdkX/f30i0hKNWvWDKGhoZg3bx4WLlyYZ73AwEB0794djRo1wrBhwzB58mSl3lRt27bF1q1bcf78edSrVw8AEBwcjBYtWiAiIkKpTX19fVhbW39w7KampnmuXxzxFqR9+/bo3r07evfujStXriAhIQFDhw7FvHnzULFixQLX37dvH5YtW4bWrVsDAJydnVGtWjX58rf31dDQEJqamir3X9W+a2trK9TV09NDRkbGBx//o0ePwsbGRuGccXFxUUgSEhERFbVK3rVxcNtaxL2KwbVzR1C3STuINXI6r1vZOGD6r1uhpa2DDEk6Jg9sB6+aDeFa0RsA8OD2FcxesRt6+ga4e+M8noXex4Axc/H9j3MAANcv/PdDz+4/A6CrZ4CFaw8BALauXYzNf/wP/X+YjcAlU1GpSm107T8WL6Ke4cfeTVCldmP1HggiIiox1tbWcHBwwJIlS+Ds7Ax7e3t4enrC2NhYZf03b96gT58+kEqlCAgIQGRkJBwcHHDo0CE4OzujYcOGSExMREBAAFxdXZXWv3DhArS1tTFgwAAAOT2XTp48iVatWinVffjwIcqXL59n7BKJBP3790dqaip+++03eHt7w9jYGDt37kSVKlVQrVo1xMXFITAwEDY2Nmjbti2uX7+O/v37K3QCeVt6ejrs7e3RpEkTnD9/HuvXr4e/vz/atWuHAwcO4MqVK2jatClMTU3Rq1cvaGpqIisrC2vXroWLiwtsbW0BABEREejbty+0tbURHh6Oly9fonXr1vL9fPz4caGOyaFDh+Do6AgfHx+8efMGK1euVHlcvwRfRFJKQ0MDc+fORffu3TFy5EjY29sr1UlOTsa2bdtw+fJlVKhQAampqTh16hQaN1a8ANPW1kaPHj0QFBSkkORZuHChwtCy4laS8S5btgyenp6YNWsWHjx4AA8PD4waNapQ61pbW+PgwYPo1KkTjIyMPmj7hd33j2VtbY2YmBicOXNGnl0vjIyMDGRkZMifJyUlFWlcRET05WvU8jucPLgVV878jdEzAnDm8A4AQEZGOlb/MhFPH92FSCxG3MtoPH18T56Uqte0PfT0//t119bBBRW9aqncxpUzfyM9NQUXT+b8QpydlQlrO2cAwN3r59H/h9kAAGs7J1Su3qCY9pSIiD5FIpEIXbp0QWxsLMLDwxEaGoqzZ89i4MCBKofteXh4QCwWQywWw9raGm/evIGDgwOePn0q/1HfxMQEZcuWVbm9kJAQZGRk4P79+wByek7lNTwwJCQEPj4+ecZeuXJlAICBgQHMzMyQkJAAHR0dvHjxAlWqVAEAWFhYwNHREREREfIRQPnR1tZGuXLlAOT0GDM2NpZ3frCxsUFYWBgAIDs7GwcPHsSLFy8gEomQmJiIFy9eyJNS7u7uComv3Dje95i8fVzNzMxQpkyZAvfhc/VFJKUAoGPHjvD29sa0adMQGKg8UefmzZvh5uYGd3d3AEC3bt0QGBioMtHRv39/1K9fH8uWLcP169eRmJiINm3aqEzyBAQE4I8//lAoW758Ofr06VOouP38/KChoSF//ueff6JDhw7FFm9hGBsbIygoCC1atICBgQH++ecflfNzqbJ69Wr06NEDFhYW8PLyQv369fHdd9/JE2aF8T77/jE6d+6Mw4cPo1GjRrC2tkbt2rXRtGlT9O7dO89fCABg3rx5mDFjRpHGQkREX5cmbbpirH9z2Dq6wNbhvwv4jSvnwcTMEovWHYOGpiYWTOyHrAyJfLmunuIUAbr5TBkgQMDAcfPhWb1+nnWIiOjrZmlpCUtLS1SvXh1//vknHj58iDp16ijVe3saF5FIpDCcrLDfFdu0aVNgciUjIwNxcXHyJI8q7xNLYWN7+zu5WCxW2IZYLJZv4/jx4zAwMMCgQYMgFouxZcsWhfmI3+2JlVfPrFyFOSZfus9+ovO3LViwAOvWrZNnGt8WGBiInj17yp/37NkTO3fuREJCglLdypUrw83NDdu3b8fatWvRq1cvaGlpqdxmjx49cOvWLYVHx44dCx3zkiVLFNbNnVyuuOItrCZNmqB27dro1asXnJycCr1ew4YNERYWhuPHj+Pbb7/FvXv30KBBA8yaNavQbbzPvn8MDQ0NBAUF4fnz51i4cCFsbW0xZ84cuLu7K8xd9a5JkyYhMTFR/oiMjCzSuIiI6MtnXsoaPYZMRq+hUxXKU5MTYWFlCw1NTUQ9C8Xtq6c/eBs16vti76aVyJCkAQAyJGmICAsBAHhUr4fj+zcBAF7FROCfa2c/eDtERPT5SUpKUpjuJT09HQkJCfnO0ayKs7Mzbt26BQBITEzE06dPVdYrV64cLl68iKysnLvPZmVl4dWrV0r1Hj9+DFdX10Ink3Lp6OjA2tpaHkt8fDwiIiLg4OAAICc5JJFI8mmhcCQSCYyNjSEWixEbGyvvQfUh8jsmZcqUwc2bNwHkzHmV13H9EnwxPaWAnISIr68vJk+eDH9/f3n5/fv3cfnyZVy9ehUTJkyQl0ulUmzatAlDhgxRaqtfv35Yvnw57t+/n++tF01MTD5qbKe1tbXS+sUZ7/vQ1NQscGJzVbS0tNCgQQM0aNAAEydOxOzZszFz5kxMmDChwEzxh+z7u4yNjZGSkgKpVKqQ8ZZKpUhJSYGJiYlCfTs7O/Tq1Qu9evXC7NmzUa5cOaxcuTLP3lA6OjrQ0dEpMA4iIqL8NG3rp1T2nf9oLJs5HGeO7ICVtQM8q314L6dOvUdga+AiTOjfWn5x36HXcDiWrYD+o2fj15kj8MOJfbBxLIvK1Qs/jJ2IiD5/MpkMZ86cwZs3b6ClpQWZTAYvL6/3vuNdy5YtsXv3bty7dw8WFhZwcHBQ+V2pfv36OH36tMIoo3r16sHKykqhXkhICKpWrfpB+9SpUyfs378fly9fBpAzX3Lud786depg/fr10NTUVJro/H00aNAAu3btwp07d2BqavpRvZzyOya5x3XFihWwsLDIc1jkl+CLSkoBwPz58+Ht7S0fDwrk9Lxp2LAhli9frlB3w4YNCAwMVJno6N69O8aOHQsvLy9UqlSp2ON+2+cWb0EqVaqE7OxsSCSSApNSH7Lv76pQoQKkUilu3ryJ6tWry8tv3LgBqVSa76R5ZmZmsLGxQWpqaoHbISIiel+rdl1TWd7t+3Hy/y/bqLp31IifflV47lG1Hv4XdCTPOpqaWug+aCK6D5qId1lY2WDG79sLHTcREX1ZTE1NFUanvGv06NHy/7/d4QMAunTpIv+/vr4+evXqBbFYjOTkZKxZswa+vr4AgA4dOsjraWhooEmTJmjSpEme25RKpXj+/Dk6depUqLgAYODAgfL/m5ubo3fv3irX8/HxyXOeKlNTU4wfP17+3NnZWaFdb29veHt7A8iZX2ro0KEq23l7f1W1826d/I6JsbFxnvvypfniklKenp7o0aMHfvvtNwA5XeA2bNiAmTNnwsPDQ6Hu999/j4ULF+L27dvw8vJSWGZmZoaYmJgCh8GlpaXhxYsXCmU6Ojrv3e0xV3HH+67Xr1/Luzjmsra2/uA72vn4+MDPzw/Vq1eHhYUF7t+/j8mTJ6Nx48b5ztMEfPi+v6tSpUpo1aoV+vXrh8WLF8PFxQVPnjzBjz/+iFatWsmTdqtWrZIPt3RxcYFEIsH69etx7949+flDREREREREqsXHx2PXrl0AcpJKjRo1gqWl5Qe1paGhoZR0oi/fFzWnVK5Zs2ZBEAQAwN69exEXF6dynic3Nzd4enqqnBgdyMmYFtStb82aNbCxsVF4+Pkpd8cvrOKO911//fUXqlSpovBYuXLlB8UOAL6+vli3bh1atGiBihUrYsSIEfD19cXWrVsLXPdj9v1dmzdvRrNmzTBkyBBUqlQJQ4YMQdOmTbFp0yZ5nZo1ayIlJQWDBw+Gu7s7GjVqhEuXLmH37t1o1KhR4XeaiIiIiIjoK1S6dGkMHjwYgwcPxrBhw1CtWrWSDok+MyIhN3tDRB8kKSkJJiYm6LC5A0IloSUdzmftWvdr0NHSwYl/4pCYll3wCkREREREhdCqqjl0tbWQcf4xhKSPn/D6UyK2MYG2tyNWrVqV7w2b6Ovx3XffwcPDA82vPsSdlPQia3egmR5mVamAxMTEAkdCFdYX2VOKiIiIiIiIiIg+bUxKFZO5c+fC0NBQ5aNVq1YlHd4Hy2ufDA0NcfYsbydNRERERERERIXzxU10/qkYPHiwwl0J3qanp6fmaIrOu5Oiv83Ozk59gRARERERERHRZ41JqWJibm4Oc3Pzkg6jyLm6upZ0CERERERERET0BeDwPSIiIiIiIiIiUjsmpYiIiIiIiIiISO2YlCIiIiIiIiIiIrVjUoqIiIiIiIiIiNSOSSkiIiIiIiIiIlI73n2PqIg4GztDS1+rpMP4rIkgAgAY6fGjiYiIiIiKjkj077+GuiUbSDEQ6WsDACwtLUs4EvpU6OrmnOduBkV7vpfWKfrvuyJBEIQib5XoK5KUlAQTExMkJibC2Ni4pMP57AmCAFHuVQMRERERURH5kq8zZTIZxGIOhKL/SAUBGkV8vhfHd1+etUT0SflSLxSIiIiIqGR9ydeZTEjRu4o6IVVceOYSEREREREREZHaMSlFRERERERERERqx6QUERERERERERGpHZNSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqR2TUkRERCVEEKQlHUKeZIKQ9zKZTI2REBEREdGXSrOkAyD6UmTeeY4MaJV0GET0mRCXtYSWjRnu3vsBaalPSjocBY6Og2Bt3QanX7xBYma2wrIWNqbQ1dLCgd9+QXxUZAlFSERERETq1mz42CJvk0kpoiIipGZAkH66vR6I6BOTkZPsSUt9guSUeyUcjKKsrDgAQGJmNuIyshSW5fafio+KxKunn1YyjYiIiIiKj6wYvu9y+B4REREREREREakdk1JERERERERERKR2TEoREREREREREZHaMSlFRERERERERERqx6QUERERERERERGpHZNSRERERERERESkdkxKERERERERERGR2jEpRUREREREREREasekFBERERERERERqV2JJqX8/f0hEokwf/58hfLdu3dDJBIp1S9fvjy0tbURFRWltMzHx0dlWwDQunVriEQiTJ8+Xan+u4/BgwcXKnZV64pEImzevBkAcOrUKXmZWCyGiYkJqlSpgvHjxyMmJkbpOHTo0EFpG7du3YJIJEJ4eLi8TBAErF69GrVq1YKhoSFMTU1RvXp1LF26FGlpaQrrP3/+HNra2qhQoYK8bPr06XnGnvsIDw/H9OnT4e3trdBefHw8Ro8eDWdnZ2hra8PGxgZ9+/ZFRESE0v68z+v6rtz183s0a9YMvr6+SusGBATAxMQEERERCq+BSCRCqVKl0KpVK9y+fVte/2PPA1Kf8v4tcS/8sUJZiwn9cPDyaQDAzA3Lse30oQLbmf1nACb+8UuxxFhYxRnDmX+u4tiNC/Ln0XGv4Duxf7Fsi4iIPg93nsdgydGzWHzkLBb+fQorT12CTBBKOiyM3XoAGVnZxb6dXw6fQVK6pNi387E2X7mNc4/DSzqMj3Yv6iX23X5QYL3Ddx9h3637aoiIiD5lJd5TSldXFwsWLMCbN2/yrXfu3DlIJBJ07twZwcHBKus4ODggKChIoSw6OhonTpyAjY2NUv0BAwYgJiZG4bFw4cJCxx4UFKS0/rvJpYcPHyI6OhpXr17FhAkTcOzYMXh4eODOnTuF3s7bevXqhdGjR+Obb77ByZMncevWLfz000/Ys2cPjhw5olA3ODgYXbp0QVpaGs6fPw8AGDt2rEK89vb2mDlzpkKZg4OD0nbj4+NRu3ZtHDt2DAEBAQgNDcWWLVvw5MkT1KhRA2FhYQr1C/u6qrJs2TKFeADlYx0UFITLly9j1apV8vWePn2KCRMmYNmyZXB0dJSXP3z4EDExMThw4ADevHmDli1bIjExUb78Y88D+jT83GsYOjdqqZZtyWQyyGQytWzrfb2blLK1sMLh+YElGBEREZWkpHQJtl+/iz51q+HHFg0wvpUP2npVRME/E36apO/59zc2ORU6mhow1tMtpohK1vseD3VwtyuNdl4VSzoMIvpMaJZ0AM2aNUNoaCjmzZuXbyIgMDAQ3bt3R6NGjTBs2DBMnjxZqddN27ZtsXXrVpw/fx716tUDkJOYadGihVJvHgDQ19eHtbX1B8duampa4PpWVlbyeuXKlcM333yDKlWqYMiQITh37tx7bW/r1q3YuHEjdu/ejW+++UZe7uzsjPbt2yMpKUleJggCgoKCEBAQAHt7ewQGBqJevXowNDSEoaGhvJ6GhgaMjIwK3I8pU6YgOjoaoaGh8rqOjo44fPgw3NzcMGzYMPz999/y+oV9XVUxMTGBiYmJQpmqY71s2TIMHz4cLVq0gLOzM/r374+mTZvC399fod7br8GiRYtQv359XLp0Sd7T6mPPA/o0DFg8FVXd3DGknR8SU5MxaMnPeBDxBDbmpWBrURpWZuaY//1YAEBM3Gt8O304wmKeo7SZBf6ashjmRjnn3JIdwdhx9gik0myUNrPEbyN+hkMpa8z+MwBhMc+RmpGOsOgI7J4ZADvL0gox5LVuYmoyBi+dhpCIMNiXsoaliRlKm1kAyOk1lSJJk8e2Yt8m3Hh8D2t+nA0A+GXbWmw+sR9isRi62jo4NO8PJKWlos/CCUhOS4EkMxM+3rWwaNAE/BP2EH/8vQ0ymQwnb13GN3WbokfTdqg3yg/PN58BABy5dg7T1v2KbKkUpobG+HX4VFR0dMGZf65i3OqFqF3RCxfv30K2VIo1P85GtXLuann9iIioeCRLMqAhEsFAW1teZm/233XW6+RU7Ll1DymSTEhlMtR2cUQ9V2cAQHjsGxz45wEk//Zm8vUoBw87a0TGJ2D3zXvIzJZCU0OM9t6VUMbSHPGpaVh69BzqujrhQcwrSLKy0aGKOyraWAHI6bF18M5D6GtroYK1lUKcf126iVfJqZDKZDDV10OXGpVhpKuD0Fdx2HvrPspamiPyTQJquzjhwO0HmNymCbQ1NQAAf168ibKlzFHX1Ulp/+9EvYCHnfJ13uvkVGy5chsZ2dkQALjblkYrz/KQymQ4dPcRQl/FQioTYGVkgG+reUJPWwvpmVnYd/sBIuITIPr3OHat6YWMrGzsvnkPEfEJAAAvBxu0cC8HAAg4eRGO5qZ4FvcGSekZcCttie+qewIAEtMk2HTlFlIzMmFuoK/Qe+3GsyicfRwuTzq18iwvP45z9p9ArbIOePwyFsZ6ukjPykI1JztUcbQDADx88RqH7j7CqGb1FPY5RZKBvy7fQpIkAyIAdmYm6FbTC1efRuJGRDR0NTURl5oGXS1N+NX0hpmBHgDgVMgT3H4eA5lMgJGuDr6r7glTfT1kS2X4++5DPHzxGiIAxnq6GNCwJq4+jcT9mFfoU7caktIl2HjpJiTZ2ciWyuBmZYlvqlQq1OgJIvo6lHhSSkNDA3PnzkX37t0xcuRI2NvbK9VJTk7Gtm3bcPnyZVSoUAGpqak4deoUGjdurFBPW1sbPXr0QFBQkEJSauHChQpD90qSnp4eBg8ejB9++AGvXr2ClZVVwSv9a+PGjShfvrxCQiqXSCRSSOScPHkSaWlpaNasGezt7VGrVi0sW7YMRkZG7x2zTCbD5s2b0aNHD6XkjZ6eHoYOHYqpU6ciPj4e5ubmAAr3un6sPn36YNeuXejbty++/fZb3L17F3fv3s13HT29nD+uWVlZRR4PFb/uc8dAV1tH/vxJtHKyGQDm/rUKZobGuL16L94kJ6HOyC7oUK+ZfPnVh3dwbtkmmBuZoNf88Qg8uA3jun6PzScPIDTqGU4v2vD/9u48rqo6/+P4+7IqqwsoKLggiErj0qKppZBbWZnVqOCeOWabWmpllllpmlNpNmJpoJPjgpnjlqYtUGpalqL+RMV9RVOIRRAVOL8/HG9eQb0g3iv4ej4e5zHD95zvOe9zOQn3w/f7vXJ0dNS875fr5env6csxUyVJP277VRs+iVO1SlULXfNafd+b95m83Dy05bMlOp3xp1oO6aEn7+943fv9z3dLtXzDD/rhwy/k5eahP7My5ersokoeDvrqrU/kUdFN+fn56vbOUP13/bd64r6OGvhQN4si16GTf013/iM9VQM+eF3fTPhcd9Str/nxX6v3hBH6ffp/JUlJh/YpeshYffz8G5r59UKN/eITLR/3qRXfGQDArcq/kpdqV62scV//oHq+VVS7amXdWaumvN0qqKDA0NyNW9SzRVNV8/LQ+bx8ffL9etWuWllV3Crq3z//rn6t7lQdnyoqMAzlnr+gvPwC/fvn39Xt7sYK9fPVgVNp+uLnzXqtc7gkKef8BQVU9taDd4RqV8ofWpqYpIb+1ZSVe05f/rZdLzzQStW8PBS/a59Fzseahcnd9WLh7Iede/Vt0h49cecdkqSU9Ew93ixMXe+8+IeS5BOntOXwMbUIqqXMs7na88dpdftfoedKO46dVI/mjQu1r997UA39q6ldo+CLuc+dlyQl7NovVydHDW1/nyTp2x17tHpHsro2C9PSxCS5Ojnq5Y73y8Fk0pnccxePSdqj/IICDe/URhfy8zXthw2q7uWpJoEXZ2qkZufo2fCWyi8o0KTVP+rg6T9Vx6eylmzZoSDfKuoYVl+pZ3L00ZqfFOrnK0kK9fNVs1o1ZDKZlJado399/7NGP/KAHB0uTnRJz8nV4PB7ZTKZlHzilNYk7TEXpdbtOaj7iijQbT50TJXdK2pQ2xYW9yxJB06n6eUO95u/N1/9vl0D2zTX5kPHdOpMtl58oLUcHEz6/eBR/XfzDj113936YddepZ7J1rD2reXk6Gh+PS5X0cVZA+67R67OTiooMDRr/W/advSE+bUBALsXpSTp8ccfV9OmTfXWW28pJqbwNJMFCxYoJCREYWEXfxBFRkYqJiamUFFKkp5++mndd999+vjjj/X7778rIyNDDz/8cJFFqejoaH3++ecWbdOmTVO/fv2syh0VFSVHR0eLtm3btikoKOia/S6t8XTw4MFiFaX27Nmj0NBQq46NiYlRZGSkHB0dFRYWpuDgYMXFxWngwIFWX++SU6dOKT09XQ0bFj0Mt2HDhjIMQ3v37lXz5s3N7df7vpaGGTNm6I477tDatWu1aNGia76eqampevvtt+Xp6WmRs7jPwblz53Tu3F8/dC8foYaba97rHyqsToj5646vDijyuJ+2/aoPB4+SJFX29NKjLR+w2N/x7vvMI6NaNGhsXqtq+YZ4bd67Q62GRkqS8vPzLf4b79y8bZEFqev1vTyPj3dlPdaqnVX3u/LXnzTo4e7ycvMw34sknbtwXm/ETtbPSVtkGIZOZaSpcVConrjv2oWuTbu3q3FQqO6oe/Gvt1ERD+ul6PeUknZKklQ/oLZ5ZFSLhk00ZfG/rcoJALh1OZhM6tf6Lv2ReUb7TqVqV8opfb9zr4a1v095BQU6mZml/2zcYj4+Ny9PJzOzlHk2V9W8PFTHp4r5PG6uLkpJz5Sjg4O5eFLXt4o8XF2Ukp4lr4qucnFyNI9Mql21slLPXFzz9HBqumpW9lY1r4s/0+4NqqWvt+0yX3fzoWP6/dAx5RUU6EJ+vjwr/PVHKB9Pd9X1rWL++v6Qulr0+3a1CKqljfsPq1mtGnJ1Lvy2JvNsrnLz8uTr6VFoX5BvFa3YulPn8/MU5FtVIdV9JEn/d/yEci/kadvRE5KkvIICVXV3kyTtTPlDw9rfJ4f/jfLx+F/GPX+cVtemYXIwmeTq5KS7a9fUnpOnzYWXpoE15OBgkoODo2pW8lJqdrbq+FTW3lOp6trs4s/dqh5uCq7mY86Xlp2jeb8kKz3nrBwdHJR9/rz+zD4rH093SdI9dQPMo43q+/lqaWKSjqdnytXJSUf/zFC/VncWuudaVSvrpz0HtCwxSfV8qyrU76/r1fWpYvG9Wb0jWYZh6P+OndTRP9M15buLMzwKDMN8/0nH/9CjTRvK6X+/73hc9j27xDCkr7ft0oHTaZKkM+fOq0YlL4pSAMxuiaKUJL3//vt64IEHNHz48EL7YmJi1Lt3b/PXvXv3Vps2bZSenq5KlSpZHNu4cWOFhIRo0aJFio+PV58+feTs7FzkNXv16qXRo0dbtBWnSDR58mS1b9/eoq2o9ZiuZPxvaG5xh60ahmFVn/T0dC1evNhiemDv3r0VGxtboqKUNbmkou/nWt/X0lCtWjUNGjRIS5Ys0eOPP17kMZdGaWVnZyskJERffvmlxfe5uM/BhAkT9Pbbb5dCetwshnHt/74qXDaFwdHRUXkF+Rf7ydBrkYPUr2PRz5L7/0baFXnNa/S91lqyTo5OFutB5J4v/FfGK0397xylZmXop8lzVcHFVa/M+KdyL1y/n2EYMhWxisiltgrOf/0y6ejgoLz8/OueEwBQNlTz8lA1Lw+1rFdbM3/6VTuOn1Son6/cXV30csf7Cx2fdPxkkecxpCLXo7r0Y9fJ4a8lax1MJvOUNENX/2F44FSa1u89qBceaCWPCq7aceykvk3668NNXJ0s37LUqlpJzo4O2vdHqn7Zf0SDw1sUed4dx08qrEbRv9M1DvBXnaqVlXzytNbvPai1yQc0sE1zGYb0xJ13mItUVrnai/I/zo5/vSYmk0kFBddfZP4/G7fo0SYNzQW+N5es0YXLfl9wueI1uS+kjtbvPagKzs5qXjfAXCi6XB2fynq5w/1K/uO0th1N0Tf/t1svdSj8vb/y5to3DFHzoOu/xynKT8n7lXP+vIa0by1nR0ctS0zi9wsAFuy+0Pklbdq0UadOnfT6669btCclJemXX37RK6+8IicnJzk5Oenee+/V2bNnNX/+/CLPNWDAAE2bNk2LFi3SgAFFj6SQLq5dFBwcbLF5eXlZndnPz69Q/6sVwC63c+fFT6OoU6eOJMnLy8ti4e1L0tPTzTklqX79+ua+1zJv3jzl5uaqRYsW5tfs1Vdf1YYNG5SUVPxPuPD19VWlSpWu2nfXrl0ymUyqV69eoX1X+76Wpkv3eDVr167V1q1blZGRoeTk5EKf2lfc52DUqFHKyMgwb0eOHCm1e0HpaNP4Hs39fpkkKf1MplZsjLeq3yMtwjVjRZzSsi7+93gh74IS913/v7nr9Q1v2lxzvl0iSUrLytCyn38w96vrH6DNe3aooKBAOblntWT9d5bn/HqhMnPOmO8lPz9ff57JVPXKVVXBxVUn/0zV4nV/fciBp5uHMrPPFJmxRcMm2rZ/t3YdvvjBBAt/XKWaPtXkV6UYv3gDAMqUjJxc8ygV6eL0urTsHFX1cJOvp7ucHR3128Gj5v2ns7KVc+686vhU0R+ZZ3Twf30LDEM5586rmqeH8goKtOfkaUnSwdNpOnPuvPy8r71ERO2qlXXszwydyrr4M+qX/X9Nwc+5cEGuzk5yc3FRXn6BNuw/dN37ui+krub9kqjqXh5FjoSSpP87drLI9aSki2tKebi66u46AXqkcUMd+t96UGE1quun5AM6n3excHI+L18nMrL+t6+aEnbvMxfaLk1XC6nuo1/2H5FhGDqXl6ffDx+zqqgVXK2qfj1w8ffItOwc7f3jtHnf2fMXVNnt4h/Dfj90VGfPX3vpibtqB2hXyin9dvCoWtYrPHVPklLP5MjFyUlNA2vo8TvDdCorW+fyLq4XdvB0msX3JrhaVZlMJoXVqK6f9x0yT/XLLyjQsT8zzK/V2uQD5iJTUdP3cs5fkGcFVzk7Oior95y2HkkpdAyA29stM1JKkiZOnKimTZuqfv365raYmBi1adNG06ZNszh2zpw5iomJ0bPPPlvoPD179tSIESPUpEkTNWrU6KbnLo6zZ89qxowZatOmjXx9Lw57btCggebPn6/c3FxVqPDXJ4Ns2rRJvr6+qly5sqSL9xUZGamlS5cWWlfKMAxlZmbK29tbMTExGj58eKEFv4cMGaLY2Fh98EHxPorewcFB3bt319y5c/XOO+9YrCt19uxZRUdHq1OnTub1pK5U1PfVlurWrVtoRN2NcHV1latr4eHJuHW83vMZDZo8Rs2e6ara1WuoZcNm8na7/npqPds9qtSsdHV6dYBMJpPy8vPVr+Pjalrv+p8gc62+o6Ke0TP/y1Ormr/aNbvX3O/x1h3033Xfqtngi1mbBIXq7P9GS/Vs96iOp51S+Mt95OzkpIquFbRy/Aw916Wnek0YoRYvdFONqtX0QNO/zvdYqwcUOW6FWrzQzbzQ+SW+3lUUM2K8nvrna8ovKJC3u6f+M6p4/x4AAMqWfKNA3+7Yo7Tss3JxclR+QYHurh1gLtYMuO8eLUtM0o+796vAMOTu6qJeLZrJ262C+re+S8sTd5oLFw/eEaqwmtXVt9VdWrplh5YlJsnJ0UF9W94pVycnZV+2RtGVPCu4qtvdf1Psut/k5uKsxgF/Td9q4OerzYeOadI3CfKuWEF1fCpr94nTVz2XJDUO8NPi37erdUidIvfnXrig01nZCqxSqcj9246kaPPhY+Y1mp686+L6VQ80rKc1O/Zo6vfrzYOfIhrUk5+3p7o0baRliUn6YPVPcnJwUGBlb3W7p7E6NArRf7fs0AerL36oSJNAf6ump3VtGqb5vyZq2+oU+Xi6WxSyHmvWSLN//l3eFSuodtXKquR29dHakv43bbK6Ms+eu+qx+06l6qfkA+YRbI80aaCKLhf/oB7kW1VrduzRycwzquDspMjmTSRJd9UJUPb5C5qesFHSxeJk87qBqlnZWxEN6mnV/+3WR9+uk5ODg7wquGpgm+YW17wvpI7mbNisj9aslVdF1+KNQANwWzAZxrUmltxc/fv3V3p6upYsWWJu69u3r7788kvl5ubq/Pnzqlmzpt555x0NHjzYou+ePXtUv359JSYmqkmTJgoPD1fTpk01ZcoUSRdHGTk7O8vd/eK866ZNm6pr167mtaXCw8NVv359vfPOOxbndXV1NReBrsVkMmnWrFl68EHLj6D39PSUu7u7eSH23bt3y8vLS1lZWfr99981adIkHT58WD/99JO5YJaRkaEGDRrovvvu06uvvqrKlStrw4YNGjJkiEaNGqWRI0dKulh4ioqK0rJly/Tmm2+qQ4cO8vX11fbt2zV58mS9+OKLqlOnjpo1a6adO3ea1666ZObMmRo9erSOHTtmHtFVp04dDRs2TMOGDbM4duzYsVqyZIkSExMlSadPn9a9996rihUratKkSbrjjjt04MABvfHGG9q9e7c2bNhgXkvret/X4j5yJpNJ//3vf9W1a9ci91+Z9ZJL34M///zzqkWpG30OJJmLgX+s3izP/FuqznvbupB3QfkFBarg4qrMnDNqN6Kf3v/HSD1wWTEIsDfHhn5yruOrX3/toqwzO+wdx0L9kLcUGNhXyw6fUuo5y7/OR9bxVUVnZ815baj+OLDvKmcAgJvrcGq65v2SqFceamte4+hyWw4f14HTaebF0su7ggJDk79dq8fvvENBvkX/ofhqLv+0PAC4lshJ/1JAnbrKyMgo1iyza7llpu9d8u6775qLFsuWLVNqamqRawWFhITob3/721UX0K5UqZK5IHU1M2fOlL+/v8UWFRVlddannnqqUP9PPvnE4pjQ0FDVqFFDd911lyZOnKj27dvr//7v/yxGcHl7e2vt2rUyDENdu3ZVkyZNNGnSJL377rsWazGZTCbNmzdPH330kf773/+qbdu2aty4scaOHavHHntMnTp1UkxMjBo1alSoICVJXbt2VVpampYvX271PV7i4+OjjRs3KiIiQs8884yCgoLUvXt3BQUFadOmTddd3P3y7+ut5kafA9x6/jyTqQdG9FWLF7rpvqFRevy+DhSkAAAoJxZu2qYvNvyux+8MK7IgJUnNatW4bQpSO46d1Hsr41XHp3KxC1IAYG92HSkFlAeMlAJQEoyUAgAAQFlyW4yUAgAAAAAAQPlHUaoI7733njw8PIrcHnroIXvHKxceeuihq77G7733nr3jAQAAAACAm4y5RkUYPHiwunfvXuS+ihWv/ckXsM7nn3+us2fPFrnvap/iBwAAAAAAyg+KUkWoUqUKhZGbrGbNmvaOAAAAAAAA7IjpewAAAAAAALA5ilIAAAAAAACwOYpSAAAAAAAAsDmKUgAAAAAAALA5ilIAAAAAAACwOYpSAAAAAAAAsDknewcAyguTu6tMcrZ3DABlhevFH8Fu7vXsHKQwZ+eqkiRvl8K/Jpj+979VagbaMBEAAADszcHRsdTPaTIMwyj1swK3kczMTHl7eysjI0NeXl72jgOgDDGMfJlMpf/DvTQUGIYcTKai9xUUyMGBwdYAAAC3k5vx3pffKAEAsJNbtSAl6aoFKUkUpAAAAFAqSvxb5Zw5c9S6dWvVqFFDhw4dkiRNmTJFS5cuLbVwAAAAAAAAKJ9KVJSaPn26Xn75ZXXu3Fnp6enKz8+XJFWqVElTpkwpzXwAAAAAAAAoh0pUlPrkk080c+ZMjR49Wo6XLXR19913a/v27aUWDgAAAAAAAOVTiYpSBw4cULNmzQq1u7q6Kjs7+4ZDAQAAAAAAoHwrUVGqbt26SkxMLNS+atUqNWrU6EYzAQAAAAAAoJxzKkmnkSNH6vnnn1dubq4Mw9Cvv/6q+fPna8KECfr8889LOyMAAAAAAADKmRIVpZ566inl5eXplVdeUU5Ojnr27KmaNWvq448/VmRkZGlnBAAAAADYkGEYMplM9o4BoJwrdlEqLy9Pc+fO1aOPPqp//OMfOn36tAoKClStWrWbkQ8oM15ZtFVHztg7BQAAAHBjwkN9NbJTA23ak6Gss3n2jgPgFtGkZumfs9hFKScnJz377LPauXOnJMnHx6fUQwFl0f7T2UpOy7d3DAAAAOCG1PN1lyRlnc1TRg5FKQCXlGiy3TWVaKHzFi1aaMuWLaWdBQAAAAAAALeJEpW5nnvuOQ0fPlxHjx7VXXfdJXd3d4v9jRs3LpVwAAAAAAAAKJ9KVJTq0aOHJGnIkCHmNpPJZF4MLz+fKUwAAAAAAAC4uhIVpQ4cOFDaOQAAAAAAAHAbKVFRqnbt2qWdAwAAAAAAALeREhWlvvjii2vu79u3b4nCAAAAAAAA4PZQoqLU0KFDLb6+cOGCcnJy5OLiIjc3N4pSAAAAAAAAuCaHknT6888/LbYzZ85o9+7duu+++zR//vzSzggAAAAAAIBypkRFqaKEhIRo4sSJhUZRAQAAAAAAAFcq0fS9q3F0dNTx48dL85QAAAC6kHZMqV9PVv7ZTDlUcFfVzi/JxaeWvWMBAFBuff7RaG1au1qnThzV5P/Eq3a9hvaOhHKoRCOlli1bZrEtXbpUn376qfr06aPWrVuXdsZS079/f5lMpkLb3r171b9/f3Xt2vW65zh69KhcXFzUoEGDIvcbhqGZM2eqZcuW8vLykoeHh8LCwjR06FDt3bv3uuf/5ptvZDKZdOLECYt2Pz8/BQYGFspiMpm0Zs0ac9uOHTvUvXt3+fr6ytXVVSEhIXrzzTeVk5Nj0bdOnTrm+69YsaIaNGigf/7znzIMw3zMwYMHZTKZlJiYaG7LyspSeHi4GjRooCNHjlz3fi5/nT09PXX33Xdr8eLF5v1jx45V06ZNC/Ur6tqS9O9//1vNmzeXu7u7PD091aZNG61YscLimISEBJlMJt1xxx3Kz8+32FepUiXNnj27yNfh8m3ixInXvTcAgO2krp4mj6YPquagGfJq/qRSV31s70gAAJRrLSMe0fjPlsnXL8DeUVCOlago1bVrV4vtiSee0NixY9W4cWPFxsaWdsZS9eCDDyolJcViq1u3rtX9Z8+ere7duysnJ0fr16+32GcYhnr27KkhQ4aoc+fOWrNmjbZt26apU6eqYsWKGjdu3HXPf99998nJyUkJCQnmtp07dyo3N1eZmZkWha34+Hg5OzubC4EbN25UixYtdP78eX399ddKTk7We++9p3//+9/q0KGDzp8/b3Gtd955RykpKdq5c6dGjBih119/XTNmzLhqtlOnTikiIkJnzpzRunXrChXJrmbWrFlKSUnRpk2b1KRJE3Xr1k0bNmywqu/lRowYoWeeeUbdu3fX1q1b9euvv+r+++/XY489pn/961+Fjt+3b991PylS+ut1uHx78cUXi50PAHBz5Gen6/zJfXIPi5AkuYW2Vl7GSeVlnLRzMgAAyq+wZi3lU62GvWOgnCvR9L2CgoLSzmEzrq6u8vPzK1FfwzA0a9YsRUdHKyAgQDExMRYjw+Li4rRgwQItXbpUXbp0MbcHBQWpXbt2FqOQrsbDw0P33HOPEhISFBkZKeniyJ/77rtPhmEoISFBwcHB5vZLo4YMw9DTTz+thg0bavHixXJwuFhvrF27turXr69mzZpp8uTJevXVV83X8vT0NL8WAwcO1PTp07VmzRo988wzhXIdOXJEHTp0kL+/v5YtWyZPT0+rX7dKlSrJz89Pfn5++vTTT7VgwQItW7ZMLVu2tPocGzdu1IcffqipU6daFIzGjx+v3Nxcvfzyy3rssccsCmUvvvii3nrrLUVFRalChQpXPfflrwMA4NaTl3VaTh5VZHJwlHRxFK6Tl6/yMk/Jybu6ndMBAACgpEo0Uuqdd94pNB1Mks6ePat33nnnhkPdquLj45WTk6P27durT58+WrhwobKyssz758+fr9DQUIuC1OVMJpNV14mIiFB8fLzFdcPDw9W2bdtC7RERF/9qnJiYqKSkJL388svmgtQlTZo0Ufv27a/6yYiXil07d+6Us7Nzof27d+9W69at1aBBA33zzTfFKkhdydnZWU5OTrpw4UKx+s2fP18eHh5FFsyGDx+uCxcu6KuvvrJoHzZsmPLy8oocRXUjzp07p8zMTIsNAHCzXfEz9Pp/5wEAAMAtrkRFqbfffltnzpwp1J6Tk6O33377hkPdTCtWrJCHh4d569atm9V9Y2JiFBkZKUdHR4WFhSk4OFhxcXHm/cnJyQoNDbXoM2zYMPO1AgKsm4sbHh6u5ORkpaSkSJJ+/PFHtW3bVm3btjVP6zty5IgOHDhgLkolJydLkho2LHrxuYYNG5qPueTVV1+Vh4eHXF1dFRERIcMwNGTIkEJ9+/btq3r16umrr76Sq6urVfdQlHPnzmncuHHKzMxUu3btzO3bt2+3+J5cWofrcsnJyapXr55cXFwKnbdGjRry9vYudH9ubm566623NGHCBGVkZFw116XX4fLt8umTV5owYYK8vb3Nm7XTGAEAJePk6aO8rNMyCi6uE2gYhvKyTsnJy9fOyQAAAHAjSlSUMgyjyFE/W7duVZUqVW441M0UERGhxMRE8zZ16lSr+qWnp2vx4sXq3bu3ua13796F1tC68nUZPXq0EhMTNWbMmCILeUVp3bq1XFxclJCQoKSkJJ09e1Z33nmn7rrrLmVmZmrPnj2Kj4+Xq6urWrVqZdU5i/qejRw5UomJifrxxx8VERGh0aNHF3m+xx57TOvWrSs0EslaUVFR8vDwkJubmz766CN98MEHeuihh8z7Q0NDLb4niYmJWrlyZbGucbVn8umnn5aPj4/ef//9q/a99DpcvrVo0eKqx48aNUoZGRnmzZoF3wEAJefoXkku1YOUvePiaOGc3evl5FWdqXsAAABlXLHWlKpcubL508nq169vUQTIz8/XmTNnNHjw4FIPWZrc3d3NazIVx7x585Sbm2tRrDAMQwUFBUpKSlKjRo0UEhKiXbt2WfTz9fWVr6+vqlWrZvW13Nzc1Lx5c8XHxystLU333XefHB0vrqPRqlUrxcfHa8OGDWrZsqV5raT69etLkpKSkor8NLtdu3YpJCTEos3Hx0fBwcEKDg7WV199peDgYN17771q3769xXGvv/66GjdurF69eskwDPXo0cPqe5GkyZMnq3379vLy8irydXBxcSn0PXFysnw069evr3Xr1un8+fOFRksdP35cmZmZhe7v0nnGjRun/v3764UXXigy36XXwVqurq43NGIMAFB8VTu9oNMrJytjw0I5uLqpaueX7B0JAIBybcY/X9Ova1crPe0PvT2kuypUdFf0oo32joVyplhFqSlTpsgwDA0YMEBvv/22vL29zftcXFxUp06dYi1eXZbExMRo+PDh6t+/v0X7kCFDFBsbqw8++EBRUVHq2bOnli5dqscee+yGrhcREaEFCxbozz//VHh4uLn90hS+DRs26KmnnjK3N23aVA0aNNDkyZMVGRlpsa7U1q1b9d1332nChAlXvV7lypX14osvasSIEdqyZUuhUUdvvPGGnJyc1KtXLxUUFCgqKsrqe/Hz8ytRIfBykZGRmjp1qj777LNCn4z3wQcfyNnZWU8++WSRfbt166Z//vOft/zUUgDA1TlXDZB/nw/tHQMAgNvGoJETNWjkRHvHQDlXrKJUv379JEl169ZVq1atilwUuyzLyMhQYmKiRVuVKlWUlpamzZs3a+7cuWrQoIHF/qioKI0ePVoTJkxQZGSkFi9erMjISI0aNUqdOnVS9erVdejQIcXFxZlHO1kjIiJC7777rlJSUjRixAhze9u2bTVx4kRlZWWZ15OSLk4b/Pzzz9WxY0c9+eSTGjVqlPz8/PTLL79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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.1 Binary and Categorical Features\n",
    "\n",
    "# Define variable columns\n",
    "binary_cols = [\"FLAG_FEMALE\", \"FLAG_OWN_CAR\", \"FLAG_OWN_REALTY\", \"FLAG_WORK_PHONE\", \"FLAG_PHONE\", \"FLAG_EMAIL\"]\n",
    "categorical_cols = [\"NAME_INCOME_TYPE\", \"NAME_EDUCATION_TYPE\", \"NAME_FAMILY_STATUS\", \"NAME_HOUSING_TYPE\", \"OCCUPATION_TYPE\"]\n",
    "numeric_cols = [\"CNT_CHILDREN\", \"AMT_INCOME_TOTAL\", \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"CNT_FAM_MEMBERS\"]\n",
    "\n",
    "# Merge categorical and 0-1 variables\n",
    "catbin_cols = list(set(categorical_cols + binary_cols))\n",
    "catbin_long = credit_application[catbin_cols].melt(var_name=\"Feature\", value_name=\"Category\")\n",
    "\n",
    "# Calculate the proportion of each value in each variable (for stacked bar chart)\n",
    "catbin_counts = (\n",
    "    catbin_long.groupby([\"Feature\", \"Category\"])\n",
    "    .size()\n",
    "    .rename(\"Proportion\")\n",
    "    .groupby(level='Feature')\n",
    "    .apply(lambda x: x / x.sum())\n",
    "    .reset_index(level=[1,2])\n",
    "    .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "# Convert to stacked bar chart structure\n",
    "stacked_data = catbin_counts.pivot(index=\"Feature\", columns=\"Category\", values=\"Proportion\").fillna(0)\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(12, 6))\n",
    "colors = sns.color_palette(\"tab20\", n_colors=stacked_data.shape[1])\n",
    "\n",
    "left = [0] * len(stacked_data)\n",
    "for i, col in enumerate(stacked_data.columns):\n",
    "    ax1.barh(stacked_data.index, stacked_data[col], left=left, color=colors[i], edgecolor='white', label=col)\n",
    "    for j, val in enumerate(stacked_data[col]):\n",
    "        if val > 0.1:\n",
    "            ax1.text(left[j] + val / 2, j, str(col), ha='center', va='center', fontsize=8, color='black')\n",
    "    left = [l + stacked_data[col].iloc[idx] for idx, l in enumerate(left)]\n",
    "\n",
    "ax1.set_title(\"Categorical/Binary Feature Distributions\", fontsize=14)\n",
    "ax1.set_xlabel(\"Proportion\")\n",
    "ax1.set_ylabel(\"Feature\")\n",
    "ax1.legend().remove()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986b567e",
   "metadata": {},
   "source": [
    "### 3.2 Discrete Numerical Features&#xA;\n",
    "#### **Scatter Plot**\n",
    "\n",
    "The scatter plot intuitively presents the original distribution of discrete numerical features such as the number of children and total income in a two-dimensional distribution form. Among them, `CNT_CHILDREN` is concentrated in the range of 0 to 2, indicating that the number of children of most samples is extremely small, with obvious aggregation at low values; `AMT_INCOME_TOTAL` shows a \"peak-like\" vertical distribution, reflecting that the total income is highly concentrated in the low and medium ranges, and there are a small number of high-income extreme values; both `DAYS_BIRTH` and `DAYS_EMPLOYED` show a single-peak vertical distribution, indicating that age and working years are relatively concentrated in the samples, with small individual differences.\n",
    "\n",
    "![Scatter Plot](Scatter_Plot.png)\n",
    "\n",
    "#### **KDE Plot**\n",
    "\n",
    "The KDE plot quantitatively shows the probability density distribution of discrete numerical features through smooth curves. `CNT_CHILDREN` shows a multi-peak distribution, with 0 children being the main peak and 1 and 2 children being the secondary peaks, reflecting the typical patterns of the number of children in families. `AMT_INCOME_TOTAL` is right-skewed, indicating that the total income is generally low, and the proportion of high-income groups is extremely small. Both `DAYS_BIRTH` and `DAYS_EMPLOYED` have a single-peak and highly concentrated distribution, with steep and narrow density curves, indicating that age and working years are highly homogeneous in the samples. `CNT_FAM_MEMBERS` shows a multi-peak discrete distribution, with peaks corresponding to 1, 2, 3, and 4 members, reflecting the diversity of family structures but overall concentration in small families.\n",
    "\n",
    "![KDE Plot](KDE_Plot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12aa8245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.2 Discrete Numerical Features\n",
    "\n",
    "# (1) Scatter Plot\n",
    "\n",
    "fig, axes = plt.subplots(1, len(numeric_cols), figsize=(12, 4))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    axes[i].scatter([0]*len(credit_application), credit_application[col], alpha=0.3, s=10)\n",
    "    axes[i].set_title(col, fontsize=9)\n",
    "    axes[i].set_xticks([])\n",
    "plt.suptitle(\"Numeric Feature Scatter Plot\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# (2) KDE Plot\n",
    "fig, axes = plt.subplots(1, len(numeric_cols), figsize=(12, 4))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    sns.kdeplot(credit_application[col], ax=axes[i], fill=True, alpha=0.4)\n",
    "plt.suptitle(\"Numeric Feature KDE Plot\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2bfbb43",
   "metadata": {},
   "source": [
    "### 3.3 Credit Label Distribution\n",
    "\n",
    "The pie chart clearly presents the proportions of the credit labels \"good\", \"medium\", and \"bad\". \"Medium\" accounts for an absolute majority at 86.84%, \"good\" accounts for 12.35%, and \"bad\" only accounts for 0.81%. This extremely unbalanced distribution causes the model to tend to predict the majority class \"medium\", resulting in poor recognition ability for the minority class (\"bad\"). In subsequent model building, methods such as oversampling, undersampling, or adjusting the loss function need to be considered to improve the data imbalance problem and enhance the model's prediction effect for different credit labels.\n",
    "\n",
    "<img src=\"pie_chart.png\" alt=\"pie_chart\" width=\"500\"> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dbf9f39d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.3 draw bar chart of Credit Label Distribution\n",
    "\n",
    "labels = credit_application[\"credit\"].value_counts().index\n",
    "sizes = credit_application[\"credit\"].value_counts().values\n",
    "colors = ['royalblue', 'darkorange', 'gold']\n",
    "explode = (0.1, 0, 0)  # explode the 'good' slice\n",
    "total = sum(sizes)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "wedges, _ = ax.pie(\n",
    "    sizes, \n",
    "    colors=colors, \n",
    "    explode=explode, \n",
    "    startangle=140,\n",
    "    textprops=dict(color=\"black\"),\n",
    "    radius=0.9\n",
    ")\n",
    "\n",
    "# Add actual values as annotations\n",
    "for i, wedge in enumerate(wedges):\n",
    "    angle = (wedge.theta2 + wedge.theta1) / 2\n",
    "    x = wedge.r * 1.2 * np.cos(np.deg2rad(angle))\n",
    "    y = wedge.r * 1.2 * np.sin(np.deg2rad(angle))\n",
    "    ax.text(x, y, f\"{sizes[i]:.0f}\", ha='center', va='center',\n",
    "            bbox=dict(boxstyle=\"round,pad=0.3\", fc=colors[i], alpha=0.7))\n",
    "\n",
    "plt.title(\"Frequency Distribution Chart\", fontsize=14, weight='bold')\n",
    "\n",
    "ax.legend(labels, loc='upper right', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3acab0a3",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 4. Logistic Regression Analysis\n",
    "\n",
    "### 4.1 Model Framework and Construction\n",
    "\n",
    "Logistic regression is a widely used econometric model for analyzing the relationship between a binary or multinomial categorical dependent variable and a set of independent variables. It estimates the probability that an observation belongs to a particular category, using the logit transformation of probabilities as a linear function of the predictors. \n",
    "\n",
    "In this study, I apply a multinomial logistic regression model to classify customers into three credit categories. Independent variables include income level, age, employment duration, and a few categorical indicators such as gender and marital status, among others. Prior to model training, categorical variables were one-hot encoded, and numerical variables were standardized. The model was estimated using maximum likelihood estimation, and its statistical significance was assessed via likelihood ratio tests. Odds ratios were used for interpretation of the effects of independent variables.\n",
    "\n",
    "### 4.2 Model Evaluation\n",
    "The likelihood ratio chi-square value is 272.353 with a p-value of 0.000***, indicating statistical significance at the 1% level. This confirms that the set of predictors jointly has a strong effect on the response variable. However, the AIC (16034.469) and BIC (16757.877) values suggest there is room to improve the model’s fit.\n",
    "\n",
    "| Likelihood Ratio Chi-square | P-value  | AIC       | BIC       |\n",
    "|-----------------------------|----------|-----------|-----------|\n",
    "| 272.353                   | 0.000*** | 16034.469 | 16757.877 |\n",
    "\n",
    "_Note: *** indicates significance at the 1% level._\n",
    "\n",
    "### 4.3 Regression Results\n",
    "\n",
    "#### **Factors positively or negatively associated with 'good' credit status :**\n",
    "\n",
    "- **`AMT_INCOME_TOTAL`**: Statistically significant (p = 0.001). The odds ratio (OR) is 1.000, indicating that higher income levels are strongly and positively associated with being classified as 'good'.\n",
    "- **`DAYS_BIRTH`**: Significant (p = 0.020), OR = 1.000. Older age reduces the likelihood of being categorized as 'good', suggesting younger clients may have better credit ratings.\n",
    "- **`NAME_INCOME_TYPE_Working`**: Significant (p = 0.033), OR = 0.909. Working individuals are 9.1% less likely to be labeled 'good' than the reference group, possibly due to income stability differences across income types.\n",
    "\n",
    "#### **Factors strongly associated with 'bad' credit status :**\n",
    "\n",
    "- **`FLAG_FEMALE`**: Significant (p = 0.026), OR = 0.693. Women are 30.7% less likely to fall into the 'bad' credit category compared to men.\n",
    "- **`CNT_CHILDREN`**: Highly significant (p = 0.000), OR = 0.043. The likelihood of being labeled 'bad' decreases drastically with more children, possibly reflecting stronger family responsibilities.\n",
    "- **`DAYS_EMPLOYED`**: Significant (p = 0.000), OR = 1.000. Longer employment duration is associated with a lower probability of poor credit, reinforcing the role of job stability.\n",
    "- **`NAME_INCOME_TYPE_Pensioner`**: Highly significant with a very large OR = 33.399, indicating pensioners are dramatically more likely to be categorized as 'bad'.\n",
    "- **`Marital Status`**: All non-single groups (e.g., separated, single, widowed) show exceedingly high odds ratios. For example, widowed individuals have an OR of 86.338, suggesting a strong link between family status and credit risk.\n",
    "\n",
    "### 4.4 Prediction Performance\n",
    "\n",
    "#### **Confusion Matrix :**\n",
    "The model exhibits excellent performance on the 'good' and 'medium' credit classes. However, it struggles with the minority class 'bad' due to severe class imbalance.\n",
    "\n",
    "<img src=\"confus_reg.png\" alt=\"confus_reg\" width=\"600\"> \n",
    "\n",
    "#### **Classification Metrics :**\n",
    "\n",
    "The  AUC suggests the model has good discriminatory power overall. However, the low recall for the 'bad' class indicates the model may miss a significant number of actual high-risk clients, which is critical in credit risk applications.\n",
    "\n",
    "| Metric | Accuracy | Recall | Precision | F1 Score | AUC |\n",
    "|--------|----------|--------|-----------|----------|-----|\n",
    "| Value  | 0.868    | 0.333  | 0.289     | 0.310    | 0.553 |\n",
    "\n",
    "### 4.5 Conclusion and Insights\n",
    "The logistic regression results offer several important insights into the factors influencing credit classification:\n",
    "\n",
    "- **Positive Predictors :** high income, younger age, and non-traditional income sources like self-employment.\n",
    "- **Negative Predictors :** being female, having more children, and being stably employed.\n",
    "- **Risk Individuals :** pensioners and those with non-married marital statuses such as separated or widowed.\n",
    "\n",
    "In summary, individuals who are young, employed, and have a higher income are more likely to have favorable credit status. In contrast, those who are retired, widowed or single, and with fewer dependents are more susceptible to credit risk. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72ec95b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.412579\n",
      "         Iterations: 100\n",
      "                          MNLogit Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                19209\n",
      "Model:                        MNLogit   Df Residuals:                    19117\n",
      "Method:                           MLE   Df Model:                           90\n",
      "Date:                Mon, 28 Jul 2025   Pseudo R-squ.:                 0.01689\n",
      "Time:                        13:43:11   Log-Likelihood:                -7925.2\n",
      "converged:                      False   LL-Null:                       -8061.4\n",
      "Covariance Type:            nonrobust   LLR p-value:                 3.180e-20\n",
      "=====================================================================================================================\n",
      "                                              y=1       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "const                                               -15.9641   1354.160     -0.012      0.991   -2670.070    2638.142\n",
      "CNT_CHILDREN                                         -0.0839      0.385     -0.218      0.827      -0.838       0.670\n",
      "AMT_INCOME_TOTAL                                      0.0768      0.024      3.187      0.001       0.030       0.124\n",
      "DAYS_BIRTH                                           -0.0591      0.026     -2.275      0.023      -0.110      -0.008\n",
      "DAYS_EMPLOYED                                        -0.0485      0.024     -1.986      0.047      -0.096      -0.001\n",
      "CNT_FAM_MEMBERS                                       0.0164      0.481      0.034      0.973      -0.927       0.960\n",
      "FLAG_FEMALE                                           0.0498      0.058      0.856      0.392      -0.064       0.164\n",
      "FLAG_OWN_CAR                                          0.0026      0.049      0.054      0.957      -0.093       0.098\n",
      "FLAG_OWN_REALTY                                      -0.0473      0.048     -0.979      0.328      -0.142       0.047\n",
      "FLAG_WORK_PHONE                                       0.0704      0.055      1.282      0.200      -0.037       0.178\n",
      "FLAG_PHONE                                           -0.0797      0.053     -1.506      0.132      -0.183       0.024\n",
      "FLAG_EMAIL                                           -0.0462      0.075     -0.618      0.536      -0.193       0.100\n",
      "NAME_INCOME_TYPE_Pensioner                          -20.1858   3.09e+04     -0.001      0.999   -6.07e+04    6.06e+04\n",
      "NAME_INCOME_TYPE_State servant                       -0.2113      0.091     -2.318      0.020      -0.390      -0.033\n",
      "NAME_INCOME_TYPE_Student                            -17.6075   6467.579     -0.003      0.998   -1.27e+04    1.27e+04\n",
      "NAME_INCOME_TYPE_Working                             -0.1380      0.050     -2.757      0.006      -0.236      -0.040\n",
      "NAME_EDUCATION_TYPE_Higher education                 14.3118   1354.160      0.011      0.992   -2639.794    2668.417\n",
      "NAME_EDUCATION_TYPE_Incomplete higher                14.3300   1354.160      0.011      0.992   -2639.776    2668.436\n",
      "NAME_EDUCATION_TYPE_Lower secondary                  14.2070   1354.160      0.010      0.992   -2639.899    2668.313\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special    14.3527   1354.160      0.011      0.992   -2639.753    2668.458\n",
      "NAME_FAMILY_STATUS_Married                            0.0338      0.079      0.427      0.669      -0.121       0.189\n",
      "NAME_FAMILY_STATUS_Separated                          0.0641      0.571      0.112      0.911      -1.056       1.184\n",
      "NAME_FAMILY_STATUS_Single / not married              -0.2235      0.566     -0.395      0.693      -1.333       0.886\n",
      "NAME_FAMILY_STATUS_Widow                             -0.1701      0.588     -0.289      0.772      -1.323       0.982\n",
      "NAME_HOUSING_TYPE_House / apartment                  -0.1004      0.290     -0.346      0.729      -0.669       0.468\n",
      "NAME_HOUSING_TYPE_Municipal apartment                 0.2295      0.311      0.739      0.460      -0.380       0.839\n",
      "NAME_HOUSING_TYPE_Office apartment                    0.4004      0.361      1.110      0.267      -0.307       1.107\n",
      "NAME_HOUSING_TYPE_Rented apartment                   -0.1320      0.334     -0.396      0.692      -0.786       0.522\n",
      "NAME_HOUSING_TYPE_With parents                       -0.0953      0.303     -0.314      0.753      -0.689       0.499\n",
      "OCCUPATION_TYPE_Cleaning staff                       -0.3563      0.193     -1.845      0.065      -0.735       0.022\n",
      "OCCUPATION_TYPE_Cooking staff                        -0.2183      0.171     -1.274      0.203      -0.554       0.118\n",
      "OCCUPATION_TYPE_Core staff                           -0.1012      0.113     -0.892      0.372      -0.324       0.121\n",
      "OCCUPATION_TYPE_Drivers                              -0.2055      0.134     -1.538      0.124      -0.467       0.056\n",
      "OCCUPATION_TYPE_HR staff                              0.1319      0.359      0.367      0.713      -0.572       0.836\n",
      "OCCUPATION_TYPE_High skill tech staff                -0.2689      0.140     -1.916      0.055      -0.544       0.006\n",
      "OCCUPATION_TYPE_IT staff                             -0.0144      0.421     -0.034      0.973      -0.840       0.812\n",
      "OCCUPATION_TYPE_Laborers                             -0.0409      0.112     -0.365      0.715      -0.261       0.179\n",
      "OCCUPATION_TYPE_Low-skill Laborers                    0.4797      0.237      2.021      0.043       0.015       0.945\n",
      "OCCUPATION_TYPE_Managers                             -0.0791      0.117     -0.679      0.497      -0.308       0.149\n",
      "OCCUPATION_TYPE_Medicine staff                       -0.3530      0.154     -2.286      0.022      -0.656      -0.050\n",
      "OCCUPATION_TYPE_Private service staff                 0.2007      0.190      1.055      0.291      -0.172       0.574\n",
      "OCCUPATION_TYPE_Realty agents                         0.2803      0.339      0.827      0.408      -0.384       0.945\n",
      "OCCUPATION_TYPE_Sales staff                          -0.2447      0.115     -2.132      0.033      -0.470      -0.020\n",
      "OCCUPATION_TYPE_Secretaries                          -0.8210      0.403     -2.039      0.041      -1.610      -0.032\n",
      "OCCUPATION_TYPE_Security staff                       -0.4222      0.194     -2.175      0.030      -0.803      -0.042\n",
      "OCCUPATION_TYPE_Waiters/barmen staff                 -0.1563      0.294     -0.531      0.595      -0.733       0.420\n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "                                              y=2       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "const                                               -26.9782   1.22e+05     -0.000      1.000   -2.39e+05    2.39e+05\n",
      "CNT_CHILDREN                                         -3.2637      0.408     -7.995      0.000      -4.064      -2.464\n",
      "AMT_INCOME_TOTAL                                      0.1114      0.087      1.282      0.200      -0.059       0.282\n",
      "DAYS_BIRTH                                            0.1245      0.093      1.331      0.183      -0.059       0.308\n",
      "DAYS_EMPLOYED                                        -0.2561      0.099     -2.591      0.010      -0.450      -0.062\n",
      "CNT_FAM_MEMBERS                                       4.0484      0.490      8.267      0.000       3.089       5.008\n",
      "FLAG_FEMALE                                          -0.3574      0.213     -1.679      0.093      -0.775       0.060\n",
      "FLAG_OWN_CAR                                         -0.0959      0.185     -0.518      0.604      -0.458       0.267\n",
      "FLAG_OWN_REALTY                                      -0.1564      0.181     -0.864      0.387      -0.511       0.198\n",
      "FLAG_WORK_PHONE                                      -0.1140      0.209     -0.545      0.586      -0.524       0.296\n",
      "FLAG_PHONE                                            0.2618      0.187      1.402      0.161      -0.104       0.628\n",
      "FLAG_EMAIL                                            0.0820      0.259      0.317      0.752      -0.426       0.589\n",
      "NAME_INCOME_TYPE_Pensioner                            3.9289      0.817      4.809      0.000       2.328       5.530\n",
      "NAME_INCOME_TYPE_State servant                       -0.6882      0.342     -2.012      0.044      -1.359      -0.018\n",
      "NAME_INCOME_TYPE_Student                            -19.5982   5.38e+04     -0.000      1.000   -1.06e+05    1.05e+05\n",
      "NAME_INCOME_TYPE_Working                             -0.2700      0.184     -1.469      0.142      -0.630       0.090\n",
      "NAME_EDUCATION_TYPE_Higher education                 20.5641   1.22e+05      0.000      1.000   -2.39e+05    2.39e+05\n",
      "NAME_EDUCATION_TYPE_Incomplete higher                20.8801   1.22e+05      0.000      1.000   -2.39e+05    2.39e+05\n",
      "NAME_EDUCATION_TYPE_Lower secondary                  21.7161   1.22e+05      0.000      1.000   -2.39e+05    2.39e+05\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special    20.3118   1.22e+05      0.000      1.000   -2.39e+05    2.39e+05\n",
      "NAME_FAMILY_STATUS_Married                            0.9198      0.463      1.986      0.047       0.012       1.828\n",
      "NAME_FAMILY_STATUS_Separated                          5.8298      0.790      7.377      0.000       4.281       7.379\n",
      "NAME_FAMILY_STATUS_Single / not married               5.6523      0.688      8.211      0.000       4.303       7.001\n",
      "NAME_FAMILY_STATUS_Widow                              6.0506      0.870      6.952      0.000       4.345       7.756\n",
      "NAME_HOUSING_TYPE_House / apartment                  -0.0849      1.023     -0.083      0.934      -2.089       1.920\n",
      "NAME_HOUSING_TYPE_Municipal apartment                 0.7459      1.069      0.698      0.485      -1.350       2.841\n",
      "NAME_HOUSING_TYPE_Office apartment                    1.0065      1.181      0.852      0.394      -1.308       3.321\n",
      "NAME_HOUSING_TYPE_Rented apartment                   -0.6883      1.256     -0.548      0.584      -3.150       1.773\n",
      "NAME_HOUSING_TYPE_With parents                       -0.8612      1.120     -0.769      0.442      -3.055       1.333\n",
      "OCCUPATION_TYPE_Cleaning staff                       -0.0511      0.783     -0.065      0.948      -1.586       1.484\n",
      "OCCUPATION_TYPE_Cooking staff                         0.3160      0.752      0.420      0.674      -1.158       1.790\n",
      "OCCUPATION_TYPE_Core staff                            1.1730      0.492      2.385      0.017       0.209       2.137\n",
      "OCCUPATION_TYPE_Drivers                               0.0045      0.590      0.008      0.994      -1.151       1.160\n",
      "OCCUPATION_TYPE_HR staff                            -21.5293   8.11e+04     -0.000      1.000   -1.59e+05    1.59e+05\n",
      "OCCUPATION_TYPE_High skill tech staff                 0.6823      0.565      1.207      0.227      -0.425       1.790\n",
      "OCCUPATION_TYPE_IT staff                              1.3292      1.123      1.184      0.236      -0.871       3.530\n",
      "OCCUPATION_TYPE_Laborers                              0.3176      0.517      0.614      0.539      -0.696       1.331\n",
      "OCCUPATION_TYPE_Low-skill Laborers                    0.0331      1.177      0.028      0.978      -2.273       2.339\n",
      "OCCUPATION_TYPE_Managers                             -0.0627      0.544     -0.115      0.908      -1.129       1.004\n",
      "OCCUPATION_TYPE_Medicine staff                        0.1561      0.700      0.223      0.823      -1.215       1.527\n",
      "OCCUPATION_TYPE_Private service staff               -30.2291   3.26e+06  -9.28e-06      1.000   -6.39e+06    6.39e+06\n",
      "OCCUPATION_TYPE_Realty agents                       -17.4521   1.15e+04     -0.002      0.999   -2.25e+04    2.25e+04\n",
      "OCCUPATION_TYPE_Sales staff                           0.0006      0.539      0.001      0.999      -1.055       1.056\n",
      "OCCUPATION_TYPE_Secretaries                           1.3259      0.857      1.546      0.122      -0.355       3.006\n",
      "OCCUPATION_TYPE_Security staff                        0.7797      0.634      1.229      0.219      -0.463       2.023\n",
      "OCCUPATION_TYPE_Waiters/barmen staff                  1.3187      0.862      1.529      0.126      -0.371       3.009\n",
      "=====================================================================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Evaluation:\n",
      "Likelihood Ratio Chi-square: 272.353\n",
      "P-value: 0.00000\n",
      "AIC: 16034.469\n",
      "BIC: 16757.877\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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6z7948WJ8/fXX6Nu3L15//XU4Oztj27Zt+Oabb7BixQooFAqTvZcHLVu27KExQ4YMwcqVKzF27FhMnjwZN2/exLvvvlvlNNzOnTtj586d2LVrF9q0aQNbW9tqjWN40OLFi/HDDz/g4MGD8PDwwNy5c5GQkICoqCh0794dvr6+NT5ndQ0YMEDqvtEnMjIS48ePx/Tp0zFq1ChcvnwZK1asqNbaCRWfyfvvv48JEybA2toafn5+Ri30ZWFhgRUrVmDcuHGIiIjAlClTUFJSgnfeeQe3bt2q1r02RkREBOLi4tChQwd06dIFqampeOedd4xeE+XcuXP4z3/+U6m8Z8+eePnll7FlyxYMGTIEb7zxBnx8fPDNN9/g448/xrRp06SpuRMmTMCqVaswfvx4vPXWW2jbti2+/fZbfPfddwCgNYakKqb6uaZGzLzjOul+aWlpYsKECaJVq1bCxsZGODg4iO7du4vXX39d5OXlSXFqtVosX75ctG/fXlhbWwtXV1cxfvx4kZ2drXW+3r17i8cff7zSdSZMmCB8fHy0ylDF7AwhykfCh4aGCgcHB9GiRQuxePFi8emnn2qNsE9MTBTPPPOM8PHxEXK5XLi4uIjevXuLffv2VbrG/bMzhBDi3LlzYujQoUKhUAgbGxvRtWvXSqPXK2Yx/N///Z9WeXVHu98/O0Ofqkaif/bZZ8LPz0/I5XLRpk0bERsbKzZu3FhphkFWVpYYMGCAcHR0FACkz1dX3e/fVzE74+DBg8LCwqLSZ3Tz5k3RqlUr0bNnT1FSUqL3PdSErnt+v6pmU2g0GrFixQrRpk0bYWtrKwIDA8WRI0eqNTtDCCEWLFggvLy8hIWFhdb713V8VbMUqvpZ+vLLL0VQUJCwtbUVDg4Ool+/fuLHH3/UiqmYgZCfn69VPmHCBOHg4FDpOg/+DlX1ngoKCkRUVJRwc3MT9vb24sknnxQ//PBDtT+PB1XE6doqjr98+bIYO3ascHFxEdbW1sLPz0+88847WjNUhBDiypUrYuTIkaJJkybC0dFRjBo1Shw4cEAAEF999VWlz+Z+un6uiSrIhKjmEG4iImoUYmJi8O9//xtXrlwx6yqy1PCxO4OIqBFbs2YNAKBDhw4oKyvDkSNH8MEHH2D8+PFMIMhoTCKIiBoxe3t7rFq1CllZWSgpKUGrVq0wf/58/Pvf/zZ31agRYHcGERERGYRTPImIiMggTCKIiIjIIEwiiIiIyCCP3MBKjUaDa9euwdHRsUZLERMRUf0ghMDt27fh5eX10AWzjHHv3r0qVxGuKRsbm0rPZWksHrkk4tq1a5WeGklERA1PdnZ2rU1TvXfvHnx9miA3T230uTw8PJCZmdkoE4lHLomoWF738unWcGrC3pzG7pn2XKKXqLFRoQzHccCo5dIfprS0FLl5alxObQ0nR8O/Kwpva+ATkIXS0lImEY1BRReGUxMLo34wqGGwklmbuwpEZGr/W5igLrqkmzjK0MTR8Oto0Li7zR+5JIKIiKi61EIDtRGrKamFxnSVqYeYRBAREemggYAGhmcRxhzbELA9n4iIiAzClggiIiIdNNDAmA4J446u/5hEEBER6aAWAmojHjFlzLENAbsziIiIyCBsiSAiItKBAyv1YxJBRESkgwYCaiYROrE7g4iIqJ6KjY2FTCZDdHS0VCaEwJIlS+Dl5QU7Ozv06dMHFy5c0DqupKQEs2bNgqurKxwcHDBs2DBcvXpVK6agoACRkZFQKBRQKBSIjIzErVu3alQ/JhFEREQ6VHRnGLMZKjk5GZ988gm6dOmiVb5ixQqsXLkSa9asQXJyMjw8PNC/f3/cvn1biomOjsbevXuxc+dOHD9+HHfu3EFERATU6r+eBTJ27FikpaUhPj4e8fHxSEtLQ2RkZI3qyCSCiIhIh4rZGcZshrhz5w7GjRuHDRs2oFmzZlK5EAKrV6/GokWLMHLkSPj7+2Pz5s24e/cutm/fDgBQKpXYuHEj3nvvPYSFhaF79+74/PPPce7cORw+fBgAkJ6ejvj4eHz66acICQlBSEgINmzYgK+//hoZGRnVrieTCCIiolpWWFiotZWUlOiNnzFjBoYMGYKwsDCt8szMTOTm5mLAgAFSmVwuR+/evXHixAkAQGpqKsrKyrRivLy84O/vL8UkJiZCoVAgKChIigkODoZCoZBiqoNJBBERkQ4aE2wA4O3tLY09UCgUiI2N1XnNnTt34vTp01XG5ObmAgDc3d21yt3d3aV9ubm5sLGx0WrBqCrGzc2t0vnd3NykmOrg7AwiIiId1EbOzqg4Njs7G05OTlK5XC6vMj47Oxv//Oc/cfDgQb2PDn/wCaZCiIc+1fTBmKriq3Oe+7ElgoiISAe1MH4DACcnJ61NVxKRmpqKvLw8BAQEwMrKClZWVkhISMAHH3wAKysrqQXiwdaCvLw8aZ+HhwdKS0tRUFCgN+b69euVrp+fn1+plUMfJhFERET1RL9+/XDu3DmkpaVJW2BgIMaNG4e0tDS0adMGHh4eOHTokHRMaWkpEhISEBoaCgAICAiAtbW1VkxOTg7Onz8vxYSEhECpVOLUqVNSzMmTJ6FUKqWY6mB3BhERkQ73j2sw9PiacHR0hL+/v1aZg4MDXFxcpPLo6GjExMSgXbt2aNeuHWJiYmBvb4+xY8cCABQKBaKiojB37ly4uLjA2dkZ8+bNQ+fOnaWBmh07dsSgQYMwadIkrF+/HgAwefJkREREwM/Pr9r1ZRJBRESkgwYyqFH9MQJVHW9qr776KoqLizF9+nQUFBQgKCgIBw8ehKOjoxSzatUqWFlZYfTo0SguLka/fv0QFxcHS0tLKWbbtm2YPXu2NItj2LBhWLNmTY3qIhOikT9i7AGFhYVQKBQo+KUNnBzZm9PYDfTqZu4qEJGJqUQZjuErKJVKrcGKplTxXXH6ojuaGPFdcee2Bj06Xa/VupoTWyKIiIh00IjyzZjjGzMmEURERDqojezOMObYhoDt+URERGQQtkQQERHpwJYI/ZhEEBER6aARMmiEEbMzjDi2IWB3BhERERmELRFEREQ6sDtDPyYRREREOqhhAbURjfZqE9alPmISQUREpIMwckyE4JgIIiIiosrYEkFERKQDx0ToxySCiIhIB7WwgFoYMSaikS97ze4MIiIiMghbIoiIiHTQQAaNEX9va9C4myKYRBAREenAMRH6sTuDiIiIDMKWCCIiIh2MH1jJ7gwiIqJHUvmYCCMewMXuDCIiIqLK2BJBRESkg8bIZ2dwdgYREdEjimMi9GMSQUREpIMGFlwnQg+OiSAiIiKDsCWCiIhIB7WQQW3E47yNObYhYBJBRESkg9rIgZVqdmcQERERVcaWCCIiIh00wgIaI2ZnaDg7g4iI6NHE7gz92J1BREREBmFLBBERkQ4aGDfDQmO6qtRLTCKIiIh0MH6xqcbd4N+43x0RERHVGrZEEBER6WD8szMa99/qjfvdERERGUEDmdFbTaxduxZdunSBk5MTnJycEBISgm+//VbaP3HiRMhkMq0tODhY6xwlJSWYNWsWXF1d4eDggGHDhuHq1ataMQUFBYiMjIRCoYBCoUBkZCRu3bpV48+HSUQDtfNDNwz06oa1r7eQyo4fUGDh823w7OP+GOjVDb+dt9M6JjfbBgO9ulW5fb9fIcXdvmWJFbNa4Rm/znjGrzNWzGqFO0rLOntvZJyICTewOSkd+38/izXxv8D/iTvmrhLVAv+gO1i6ORPbT1/Ad9d+Qsggpbmr1ChVtEQYs9VEy5YtsWzZMqSkpCAlJQVPP/00hg8fjgsXLkgxgwYNQk5OjrQdOHBA6xzR0dHYu3cvdu7ciePHj+POnTuIiIiAWq2WYsaOHYu0tDTEx8cjPj4eaWlpiIyMrPHnY/Yk4uOPP4avry9sbW0REBCAH374QW98QkICAgICYGtrizZt2mDdunV1VNP6IyPNDgc+d4Fvp2Kt8nt3LdCpZxFeXHityuOae5ViR9p5rS1yXg5s7dXo+fRtKW7ZDB/8dsEOb2/7DW9v+w2/XbDDilmtavU9kWn0HlaAqUuvYccHbpg+oD3On3TAW9sy0bxFqbmrRiZma6/B7xds8dGiFg8PpgZj6NChGDx4MNq3b4/27dvj7bffRpMmTZCUlCTFyOVyeHh4SJuzs7O0T6lUYuPGjXjvvfcQFhaG7t274/PPP8e5c+dw+PBhAEB6ejri4+Px6aefIiQkBCEhIdiwYQO+/vprZGRk1Ki+Zk0idu3ahejoaCxatAhnzpxBr169EB4ejitXrlQZn5mZicGDB6NXr144c+YMFi5ciNmzZ2P37t11XHPzKS6ywPKZPoh+JxuOCrXWvrC/F2D8nOvo/lTVf3laWgLObiqt7cS3CvQedgt2DuUTka5ckiPlqBNefjcbnQLvolPgXUS/k42ThxXI/lVe6++PjDNy8g18t8MZ8dtdkP2rLdYtboH8a9aI+MdNc1eNTCzlqBM2r/DEj982NXdVGrWKxaaM2Qy+tlqNnTt3oqioCCEhIVL5sWPH4Obmhvbt22PSpEnIy8uT9qWmpqKsrAwDBgyQyry8vODv748TJ04AABITE6FQKBAUFCTFBAcHQ6FQSDHVZdYkYuXKlYiKisJLL72Ejh07YvXq1fD29sbatWurjF+3bh1atWqF1atXo2PHjnjppZfw4osv4t13363jmpvPmoUt8US/QvTQkSjUxKWzdvjtgj0GPv/XF0x6igMcnNTo0OOuVNYx4C4cnNS4mOJg9DWp9lhZa9Cuy12kJjhqlacmOKJTYJGZakXUsGmEzOgNAAoLC7W2kpISndc8d+4cmjRpArlcjqlTp2Lv3r3o1KkTACA8PBzbtm3DkSNH8N577yE5ORlPP/20dL7c3FzY2NigWbNmWud0d3dHbm6uFOPm5lbpum5ublJMdZktiSgtLUVqaqpWtgQAAwYM0JkJJSYmVoofOHAgUlJSUFZWVmt1rS+OfdkUv56zw4sLckxyvvgdLmjV7h4e7/lXwvBnvhWaulb+LJu6lqEgn5N56jMnZzUsrYBbN7Tv0618KzRzU5mpVkQEAN7e3tIgRoVCgdjYWJ2xfn5+SEtLQ1JSEqZNm4YJEybg4sWLAIAxY8ZgyJAh8Pf3x9ChQ/Htt9/il19+wTfffKP3+kIIyGR/DfK8/9+6YqrDbN8KN27cgFqthru7u1b5/dnSg3Jzc6uMV6lUuHHjBjw9PSsdU1JSopXxFRYWmqD2dS/vD2usfb0FYnb8Bhtb49diLymW4ejeZhgbXfmzrupHSAhZDccYk7k8+LwfmQxo5Mv3E9UajZFdEhWLTWVnZ8PJyUkql8t1dw/b2Nigbdu2AIDAwEAkJyfj/fffx/r16yvFenp6wsfHB5cuXQIAeHh4oLS0FAUFBVqtEXl5eQgNDZVirl+/Xulc+fn5lb5jH8bsAysfzHoelglVFV9VeYXY2Fit7M/b29vIGpvHr2ftceuGNWYO8kO4d1eEe3fF2cQm+GqjK8K9u0Ktfvg57vfDN01RUixD2LN/apU7N1eh4IZ1pXjlTSs0bc6/Zuuzwj8toVYBzR64TwpXFVuRiAxU8RRPYzYA0pTNik1fEvEgIYTO7o+bN28iOztb+iM6ICAA1tbWOHTokBSTk5OD8+fPS0lESEgIlEolTp06JcWcPHkSSqVSiqkus/3P4urqCktLy0qtDnl5eTozIQ8Pjyrjrays4OLiUuUxCxYswJw5c6TXhYWFDTKR6NbrNtYf+Vmr7L2XW8G77T2MnpEHyxrOwPxuhwuCBxSiqYt29tExsAhFhZb4+Yw9OnQv7+b4+bQ9igot2a9ez6nKLHDprD16PHUbJ+L/mrLb46nbSPxOoedIIqovFi5ciPDwcHh7e+P27dvYuXMnjh07hvj4eNy5cwdLlizBqFGj4OnpiaysLCxcuBCurq545plnAAAKhQJRUVGYO3cuXFxc4OzsjHnz5qFz584ICwsDAHTs2BGDBg3CpEmTpNaNyZMnIyIiAn5+fjWqr9mSCBsbGwQEBODQoUPSmweAQ4cOYfjw4VUeExISgv3792uVHTx4EIGBgbC2rvzXM1DeZFSTjK++sm+iQesO97TKbO01cGymlsoLCyyR/4cNbl4vv63Zv5W/72ZuZXC+r0/8j0wbnEtywJuf/17pOq3alSCwbyFWv+KNfy7PBgC8/6o3gsKU8G6reyAQ1Q97PnHFKx9k45ezdkhPccDg8Tfh1qIM32ypOsmmhsvWXg0v37+m7np4l6LN48W4fav8/wEyDTVkUBvRmVvTY69fv47IyEjk5ORAoVCgS5cuiI+PR//+/VFcXIxz585hy5YtuHXrFjw9PdG3b1/s2rULjo5/DahetWoVrKysMHr0aBQXF6Nfv36Ii4uD5X1/bW7btg2zZ8+WxhkOGzYMa9asqfH7kwnxYA9q3dm1axciIyOxbt06hISE4JNPPsGGDRtw4cIF+Pj4YMGCBfjjjz+wZcsWAOVTPP39/TFlyhRMmjQJiYmJmDp1Knbs2IFRo0ZV65qFhYVQKBQo+KUNnBzN3ptjlFdGtUWbx4sx7Y0/AAAHdznjvZcrr+cwfk4uIuf91YLzWawn/vufZtiafBEWVXwEhQWWWPtaCyQdLP/rNXiAEjPe/gNNFDXsM6kHBnp1M3cV6lzEhBt4dnoenN1UuJxhi3WLvXD+ZBNzV4tMrEvIHbyz+7dK5Qd3Navy/4HGRCXKcAxfQalUao0zMKWK74qlJ8Ng28Twv7fv3VFhcdDhWq2rOZk1iQDKF5tasWIFcnJy4O/vj1WrVuGpp54CUL68Z1ZWFo4dOybFJyQk4OWXX8aFCxfg5eWF+fPnY+rUqdW+XmNKIujhHsUkgqixYxJRf5h9tNX06dMxffr0KvfFxcVVKuvduzdOnz5dy7UiIiIC1Kh5l8SDxzdmZk8iiIiI6qv7Z1gYenxjxiSCiIhIBz4KXL/G/e6IiIio1rAlgoiISAcBGTRGjIkQjXytXyYRREREOrA7Q7/G/e6IiIio1rAlgoiISIf7H+dt6PGNGZMIIiIiHdRGPsXTmGMbgsb97oiIiKjWsCWCiIhIB3Zn6MckgoiISAcNLKAxotHemGMbgsb97oiIiKjWsCWCiIhIB7WQQW1El4QxxzYETCKIiIh04JgI/ZhEEBER6SCMfIqn4IqVRERERJWxJYKIiEgHNWRQG/EQLWOObQiYRBAREemgEcaNa9AIE1amHmJ3BhERERmELRFEREQ6aIwcWGnMsQ0BkwgiIiIdNJBBY8S4BmOObQgad4pEREREtYYtEURERDpwxUr9mEQQERHpwDER+jXud0dERES1hi0RREREOmhg5LMzGvnASiYRREREOggjZ2cIJhFERESPJj7FUz+OiSAiIiKDsCWCiIhIB87O0I9JBBERkQ7sztCvcadIREREVGuYRBAREelQ8ewMY7aaWLt2Lbp06QInJyc4OTkhJCQE3377rbRfCIElS5bAy8sLdnZ26NOnDy5cuKB1jpKSEsyaNQuurq5wcHDAsGHDcPXqVa2YgoICREZGQqFQQKFQIDIyErdu3arx58MkgoiISIeK7gxjtppo2bIlli1bhpSUFKSkpODpp5/G8OHDpURhxYoVWLlyJdasWYPk5GR4eHigf//+uH37tnSO6Oho7N27Fzt37sTx48dx584dREREQK1WSzFjx45FWloa4uPjER8fj7S0NERGRtb485EJIUSNj2rACgsLoVAoUPBLGzg5Modq7AZ6dTN3FYjIxFSiDMfwFZRKJZycnGrlGhXfFUO+ewnWDjYGn6esqBTfDPzUqLo6OzvjnXfewYsvvggvLy9ER0dj/vz5AMpbHdzd3bF8+XJMmTIFSqUSzZs3x9atWzFmzBgAwLVr1+Dt7Y0DBw5g4MCBSE9PR6dOnZCUlISgoCAAQFJSEkJCQvDzzz/Dz8+v2nXjtygREZEOpmqJKCws1NpKSkoeem21Wo2dO3eiqKgIISEhyMzMRG5uLgYMGCDFyOVy9O7dGydOnAAApKamoqysTCvGy8sL/v7+UkxiYiIUCoWUQABAcHAwFAqFFFNdTCKIiIh0MFUS4e3tLY0/UCgUiI2N1XnNc+fOoUmTJpDL5Zg6dSr27t2LTp06ITc3FwDg7u6uFe/u7i7ty83NhY2NDZo1a6Y3xs3NrdJ13dzcpJjq4hRPIiKiWpadna3VnSGXy3XG+vn5IS0tDbdu3cLu3bsxYcIEJCQkSPtlMu1xFkKISmUPejCmqvjqnOdBbIkgIiLSwVQtERWzLSo2fUmEjY0N2rZti8DAQMTGxqJr1654//334eHhAQCVWgvy8vKk1gkPDw+UlpaioKBAb8z169crXTc/P79SK8fDMIkgIiLSQcC4aZ6mmLkghEBJSQl8fX3h4eGBQ4cOSftKS0uRkJCA0NBQAEBAQACsra21YnJycnD+/HkpJiQkBEqlEqdOnZJiTp48CaVSKcVUF7sziIiIdKjrFSsXLlyI8PBweHt74/bt29i5cyeOHTuG+Ph4yGQyREdHIyYmBu3atUO7du0QExMDe3t7jB07FgCgUCgQFRWFuXPnwsXFBc7Ozpg3bx46d+6MsLAwAEDHjh0xaNAgTJo0CevXrwcATJ48GRERETWamQEwiSAiIqo3rl+/jsjISOTk5EChUKBLly6Ij49H//79AQCvvvoqiouLMX36dBQUFCAoKAgHDx6Eo6OjdI5Vq1bBysoKo0ePRnFxMfr164e4uDhYWlpKMdu2bcPs2bOlWRzDhg3DmjVralxfrhNBjRrXiSBqfOpynYg+X0+DlYPu8QsPoyoqwbGItbVaV3NiSwQREZEOfACXfvxTnIiIiAzClggiIiId2BKhH5MIIiIiHYSQQRiRCBhzbEPA7gwiIiIyCFsiiIiIdKhYNMqY4xszJhFEREQ6cEyEfuzOICIiIoOwJYKIiEgHDqzUj0kEERGRDuzO0I9JBBERkQ5sidCPYyKIiIjIII9sS8Sovz8LK0vDH6pCDcVFc1eAiBowYWR3RmNviXhkkwgiIqKHEQCMedZ1Y39MNrsziIiIyCBsiSAiItJBAxlkXLFSJyYRREREOnB2hn7sziAiIiKDsCWCiIhIB42QQcbFpnRiEkFERKSDEEbOzmjk0zPYnUFEREQGYUsEERGRDhxYqR+TCCIiIh2YROjHJIKIiEgHDqzUj2MiiIiIyCBsiSAiItKBszP0YxJBRESkQ3kSYcyYCBNWph5idwYREREZhC0RREREOnB2hn5MIoiIiHQQ/9uMOb4xY3cGERERGYQtEURERDqwO0M/JhFERES6sD9DL3ZnEBER6fK/lghDN9SwJSI2NhY9e/aEo6Mj3NzcMGLECGRkZGjFTJw4ETKZTGsLDg7WiikpKcGsWbPg6uoKBwcHDBs2DFevXtWKKSgoQGRkJBQKBRQKBSIjI3Hr1q0a1ZdJBBERUT2RkJCAGTNmICkpCYcOHYJKpcKAAQNQVFSkFTdo0CDk5ORI24EDB7T2R0dHY+/evdi5cyeOHz+OO3fuICIiAmq1WooZO3Ys0tLSEB8fj/j4eKSlpSEyMrJG9WV3BhERkQ51vWJlfHy81utNmzbBzc0NqampeOqpp6RyuVwODw+PKs+hVCqxceNGbN26FWFhYQCAzz//HN7e3jh8+DAGDhyI9PR0xMfHIykpCUFBQQCADRs2ICQkBBkZGfDz86tWfdkSQUREpIMxXRn3D8osLCzU2kpKSqp1faVSCQBwdnbWKj927Bjc3NzQvn17TJo0CXl5edK+1NRUlJWVYcCAAVKZl5cX/P39ceLECQBAYmIiFAqFlEAAQHBwMBQKhRRTHUwiiIiIapm3t7c09kChUCA2NvahxwghMGfOHDz55JPw9/eXysPDw7Ft2zYcOXIE7733HpKTk/H0009LiUlubi5sbGzQrFkzrfO5u7sjNzdXinFzc6t0TTc3NymmOtidQUREpIsBgyMrHQ8gOzsbTk5OUrFcLn/ooTNnzsTZs2dx/PhxrfIxY8ZI//b390dgYCB8fHzwzTffYOTIkbqrIgRksr/ey/3/1hXzMGyJICIi0qFiTIQxGwA4OTlpbQ9LImbNmoV9+/bh6NGjaNmypd5YT09P+Pj44NKlSwAADw8PlJaWoqCgQCsuLy8P7u7uUsz169crnSs/P1+KqQ4mEURERPWEEAIzZ87Enj17cOTIEfj6+j70mJs3byI7Oxuenp4AgICAAFhbW+PQoUNSTE5ODs6fP4/Q0FAAQEhICJRKJU6dOiXFnDx5EkqlUoqpDnZnEBER6VLHi03NmDED27dvx1dffQVHR0dpfIJCoYCdnR3u3LmDJUuWYNSoUfD09ERWVhYWLlwIV1dXPPPMM1JsVFQU5s6dCxcXFzg7O2PevHno3LmzNFujY8eOGDRoECZNmoT169cDACZPnoyIiIhqz8wAqplEfPDBB9U+4ezZs6sdS0REVJ/V9bLXa9euBQD06dNHq3zTpk2YOHEiLC0tce7cOWzZsgW3bt2Cp6cn+vbti127dsHR0VGKX7VqFaysrDB69GgUFxejX79+iIuLg6WlpRSzbds2zJ49W5rFMWzYMKxZs6ZG9ZUJ8fBZrNVpTgHKB2n8/vvvNapAXSssLIRCocDTXebDyvLhA1uoYdOkXTR3FYjIxFSiDMfwFZRKpdZgRVOq+K5o9cnrsLC3Nfg8mrv3cGXyG7VaV3OqVktEZmZmbdeDiIiofmrkz78whsEDK0tLS5GRkQGVSmXK+hAREdUbplpsqrGqcRJx9+5dREVFwd7eHo8//jiuXLkCoHwsxLJly0xeQSIiIrMRJtgasRonEQsWLMBPP/2EY8eOwdb2r36isLAw7Nq1y6SVIyIiovqrxlM8v/zyS+zatQvBwcFaq1p16tQJv/32m0krR0REZF6y/23GHN941TiJyM/Pr3K97aKiohotlUlERFTv1fE6EQ1NjbszevbsiW+++UZ6XZE4VDxClIiIiB4NNW6JiI2NxaBBg3Dx4kWoVCq8//77uHDhAhITE5GQkFAbdSQiIjIPtkToVeOWiNDQUPz444+4e/cuHnvsMRw8eBDu7u5ITExEQEBAbdSRiIjIPCqe4mnM1ogZ9OyMzp07Y/PmzaauCxERETUgBiURarUae/fuRXp6OmQyGTp27Ijhw4fDyorP8yIiosbj/sd5G3p8Y1bjb/3z589j+PDhyM3NlZ709csvv6B58+bYt28fOnfubPJKEhERmQXHROhV4zERL730Eh5//HFcvXoVp0+fxunTp5GdnY0uXbpg8uTJtVFHIiIiqodq3BLx008/ISUlBc2aNZPKmjVrhrfffhs9e/Y0aeWIiIjMytjBkY18YGWNWyL8/Pxw/fr1SuV5eXlo27atSSpFRERUH8iE8VtjVq2WiMLCQunfMTExmD17NpYsWYLg4GAAQFJSEt544w0sX768dmpJRERkDhwToVe1koimTZtqLWkthMDo0aOlMvG/4adDhw6FWq2uhWoSERFRfVOtJOLo0aO1XQ8iIqL6h2Mi9KpWEtG7d+/argcREVH9w+4MvQxeHeru3bu4cuUKSktLtcq7dOlidKWIiIio/jPoUeAvvPACvv322yr3c0wEERE1GmyJ0KvGUzyjo6NRUFCApKQk2NnZIT4+Hps3b0a7du2wb9++2qgjERGReQgTbI1YjVsijhw5gq+++go9e/aEhYUFfHx80L9/fzg5OSE2NhZDhgypjXoSERFRPVPjloiioiK4ubkBAJydnZGfnw+g/Mmep0+fNm3tiIiIzImPAterxi0Rfn5+yMjIQOvWrdGtWzesX78erVu3xrp16+Dp6VkbdSQdxo07h/HjzmuV/fmnLcaNfwYA0LRpMV584Sf06JELB4dSnD/fHGvXBeLaNUcpftbMU+je/TqcnYtx754VLl50xWebuuHqVac6fS9kOhETbuDZaflwdivD5V9sse51L5w/1cTc1aJawHtd+4xddZIrVj4gOjoaOTk5AIDFixdj4MCB2LZtG2xsbBAXF2fq+tFDZGUpsHBRX+m1Rl2R9Qq8/toPUKkt8MYbvVB01xojn/kZMTFHMGXKEJSUlN/6X391xtFjrZGXZw9Hx1KMH3ceb791FC+8OBQaTY0bqsjMeg8rwNSl17BmYQtcOOWAIZE38da2TEzq44f8P2zMXT0yId5rqg9q/C0xbtw4TJw4EQDQvXt3ZGVlITk5GdnZ2RgzZkyNzvX9999j6NCh8PLygkwmw5dffvnQYxISEhAQEABbW1u0adMG69atq+lbaFTUahkKCuykTVloCwBo0eI2Ona8iTVreuKXSy744w8nfPRxIOxsVejT57J0/LfxbXH+vBvy8prgt9+csXlLZ7i53YW7W5G53hIZYeTkG/huhzPit7sg+1dbrFvcAvnXrBHxj5vmrhqZGO91HeHASr2M/lPT3t4ePXr0gKura42PLSoqQteuXbFmzZpqxWdmZmLw4MHo1asXzpw5g4ULF2L27NnYvXt3ja/dWLRocRufb/0Smz7bh3/N/xEeHncAANbWGgBAWelft1ijsYBKZYHHO+VXeS65XIUB/TORk+OA/Bv2tV95Mikraw3adbmL1ARHrfLUBEd0CmRS2JjwXlN9Ua3ujDlz5lT7hCtXrqx2bHh4OMLDw6sdv27dOrRq1QqrV68GAHTs2BEpKSl49913MWrUqGqfp7HIyHDBu++F4I8/HNG06T08/9wFvPfuIUydNhjZ2U64ft0BE1/4CR9++ATu3bPEM89kwNn5Hpydi7XOM2TIJUS9mAY7OxWuXHHCokV9oVJZmuldkaGcnNWwtAJu3dD+tb6Vb4Vmbioz1YpqA+913ZHByDERJqtJ/VStJOLMmTPVOtn9D+mqDYmJiRgwYIBW2cCBA7Fx40aUlZXB2tq60jElJSUoKSmRXt//RNKGLiXFS+t1erorPtu4H2Fhmdi7twPeevtJRP/zJP7vi91Qq2U4c8YdycmVB78ePeqDM2c84OxcjFEjf8aCBT9i7rz+KCtjItEQiQf+w5PJ0OibVB9VvNdkbg3qAVy5ublwd3fXKnN3d4dKpcKNGzeqnB0SGxuLpUuX1lUVzaqkxApZlxVo4XUbQPmgyZmzwmFvXwprKw2UhbZYteogLl1y1jru7l0b3L1rg2vXHPHzzy74vy92IzQ0GwkJrc3wLshQhX9aQq0CmjXX/ktU4apCQb7BK9xTPcR7XYf4AC69Gtzw+wdbOyoeQ66rFWTBggVQKpXSlp2dXet1NBdrKzVaeRfizz/ttMrv3rWBstAWXl630a7tn0hKbPHwc/1vTAU1HKoyC1w6a48eT93WKu/x1G1cTHEwU62oNvBe1yEOrNSrQaWsHh4eyM3N1SrLy8uDlZUVXFxcqjxGLpdDLpfXRfXq3EtRZ3DyZAvk5dtLYyLs7ctw+L++AIAnn7wCpVKO/HwHtG59C1OnnEZiUgucPlPeYuPhcQdPPXUZp097QqmUw8WlGM8+exGlpZZITvbSd2mqp/Z84opXPsjGL2ftkJ7igMHjb8KtRRm+2VL17wc1XLzXVB80qCQiJCQE+/fv1yo7ePAgAgMDqxwP0di5ut7F/Pkn4ORUAqVSjp8zXPDyywOQl1f+l4izczEmTzqDpk3v4c8CW/z3v77YseNx6fjSUgv4P56PEcMz0KRJGW7dssX5880xZ25/KJW25npbZISEfc3g2EyNcS9fh7ObCpczbPHv8b7I47oBjQ7vdR2p4wdwxcbGYs+ePfj5559hZ2eH0NBQLF++HH5+fn+dUggsXboUn3zyCQoKChAUFISPPvoIjz/+1//vJSUlmDdvHnbs2IHi4mL069cPH3/8MVq2bCnFFBQUYPbs2dJzr4YNG4YPP/wQTZs2rXZ9ZUI8ODSn7ty5cwe//vorgPI1J1auXIm+ffvC2dkZrVq1woIFC/DHH39gy5YtAMqnePr7+2PKlCmYNGkSEhMTMXXqVOzYsaPaszMKCwuhUCjwdJf5sLJsnC0U9BdN2kVzV4GITEwlynAMX0GpVMLJqXZW1634rmj99tuwsDX8jyrNvXvIWrSo2nUdNGgQnnvuOfTs2RMqlQqLFi3CuXPncPHiRTg4lP+BuHz5crz99tuIi4tD+/bt8dZbb+H7779HRkYGHB3Lp/1OmzYN+/fvR1xcHFxcXDB37lz8+eefSE1NhaVl+aD58PBwXL16FZ988gkAYPLkyWjdunWlP9b1MWsScezYMfTt27dS+YQJExAXF4eJEyciKysLx44dk/YlJCTg5ZdfxoULF+Dl5YX58+dj6tSp1b4mk4hHC5MIosanMScRD8rPz4ebmxsSEhLw1FNPQQgBLy8vREdHY/78+QDKWx3c3d2xfPlyTJkyBUqlEs2bN8fWrVulRSCvXbsGb29vHDhwAAMHDkR6ejo6deqEpKQkBAUFAQCSkpIQEhKCn3/+WavlQx+DBlZu3boVf/vb3+Dl5YXLl8tXP1y9ejW++uqrGp2nT58+EEJU2iqWz46Li9NKIACgd+/eOH36NEpKSpCZmVmjBIKIiKhGTDSwsrCwUGu7f+kBfZRKJYDyB14C5S3yubm5WssdyOVy9O7dGydOnAAApKamoqysTCvGy8sL/v7+UkxiYiIUCoWUQABAcHAwFAqFFFMdNU4i1q5dizlz5mDw4MG4desW1Go1AKBp06bSIlBERESNgomSCG9vbygUCmmLjY19+KWFwJw5c/Dkk0/C398fAKTJBVUtd1CxLzc3FzY2NmjWrJnemIonct/Pzc2t0gQGfWo8sPLDDz/Ehg0bMGLECCxbtkwqDwwMxLx582p6OiIiokYvOztbqzujOrMGZ86cibNnz+L48eOV9lW13MHDFnx8MKaq+Oqc5341bonIzMxE9+7dK5XL5XIUFXHNdiIiajwqHgVuzAYATk5OWtvDkohZs2Zh3759OHr0qNaMCg8PDwCocrmDitYJDw8PlJaWoqCgQG/M9evXK103Pz+/UiuHPjVOInx9fZGWllap/Ntvv0WnTp1qejoiIqL6q2LFSmO2mlxOCMycORN79uzBkSNH4Ovrq7Xf19cXHh4eOHTokFRWWlqKhIQEhIaGAgACAgJgbW2tFZOTk4Pz589LMSEhIVAqlTh16pQUc/LkSSiVSimmOmrcnfHKK69gxowZuHfvHoQQOHXqFHbs2IHY2Fh8+umnNT0dERFR/VXH60TMmDED27dvx1dffQVHR0epxUGhUMDOzg4ymQzR0dGIiYlBu3bt0K5dO8TExMDe3h5jx46VYqOiojB37ly4uLjA2dkZ8+bNQ+fOnREWFgag/AGWgwYNwqRJk7B+/XoA5VM8IyIiqj0zAzAgiXjhhRegUqnw6quv4u7duxg7dixatGiB999/H88991xNT0dERET/s3btWgDlsxfvt2nTJkycOBEA8Oqrr6K4uBjTp0+XFps6ePCgtEYEAKxatQpWVlYYPXq0tNhUXFyctEYEAGzbtg2zZ8+WZnEMGzYMa9asqVF9jVon4saNG9BoNFWO8KyvuE7Eo4XrRBA1PnW5TkSbxTFGrxPx+9KFtVpXczJq2WtXV1dT1YOIiKj+qePujIamxkmEr6+v3ukfv//+u1EVIiIiooahxklEdHS01uuysjKcOXMG8fHxeOWVV0xVLyIiIvO7b5qmocc3ZjVOIv75z39WWf7RRx8hJSXF6AoRERHVG+zO0MugZ2dUJTw8HLt37zbV6YiIiKieM2pg5f3+85//SA8IISIiahTYEqFXjZOI7t27aw2sFEIgNzcX+fn5+Pjjj01aOSIiInOSGTkmwqjxFA1AjZOIESNGaL22sLBA8+bN0adPH3To0MFU9SIiIqJ6rkZJhEqlQuvWrTFw4EDpISBERET0aKrRwEorKytMmzYNJSUltVUfIiKi+kOYYGvEajw7IygoCGfOnKmNuhAREdUrpnoUeGNV4zER06dPx9y5c3H16lUEBATAwcFBa3+XLl1MVjkiIiKqv6qdRLz44otYvXo1xowZAwCYPXu2tE8mk0EIAZlMBrVabfpaEhERmUsjb00wRrWTiM2bN2PZsmXIzMyszfoQERHVH1wnQq9qJxEVTwz38fGptcoQERFRw1GjMRH6nt5JRETU2HCxKf1qlES0b9/+oYnEn3/+aVSFiIiI6g12Z+hVoyRi6dKlUCgUtVUXIiIiakBqlEQ899xzcHNzq626EBER1SvsztCv2kkEx0MQEdEjh90ZelV7xcqK2RlEREREQA1aIjQaTW3Wg4iIqP5hS4ReNV72moiI6FHBMRH6MYkgIiLShS0RetX4KZ5EREREAFsiiIiIdGNLhF5MIoiIiHTgmAj92J1BREREBmFLBBERkS7sztCLSQQREZEO7M7Qj90ZREREZBC2RBAREenC7gy9mEQQERHpwiRCL3ZnEBER1SPff/89hg4dCi8vL8hkMnz55Zda+ydOnAiZTKa1BQcHa8WUlJRg1qxZcHV1hYODA4YNG4arV69qxRQUFCAyMhIKhQIKhQKRkZG4detWjerKJIKIiEgHmQm2mioqKkLXrl2xZs0anTGDBg1CTk6OtB04cEBrf3R0NPbu3YudO3fi+PHjuHPnDiIiIqBWq6WYsWPHIi0tDfHx8YiPj0daWhoiIyNrVFd2ZxAREelihu6M8PBwhIeH642Ry+Xw8PCocp9SqcTGjRuxdetWhIWFAQA+//xzeHt74/Dhwxg4cCDS09MRHx+PpKQkBAUFAQA2bNiAkJAQZGRkwM/Pr1p1ZUsEERGRDhVTPI3ZasOxY8fg5uaG9u3bY9KkScjLy5P2paamoqysDAMGDJDKvLy84O/vjxMnTgAAEhMToVAopAQCAIKDg6FQKKSY6mBLBBERUS0rLCzUei2XyyGXyw06V3h4OJ599ln4+PggMzMTr732Gp5++mmkpqZCLpcjNzcXNjY2aNasmdZx7u7uyM3NBQDk5ubCzc2t0rnd3NykmOpgEkFERKSLibozvL29tYoXL16MJUuWGHTKMWPGSP/29/dHYGAgfHx88M0332DkyJG6qyIEZLK/Rmnc/29dMQ/DJIKIiEgfE3RJZGdnw8nJSXptaCtEVTw9PeHj44NLly4BADw8PFBaWoqCggKt1oi8vDyEhoZKMdevX690rvz8fLi7u1f72hwTQUREVMucnJy0NlMmETdv3kR2djY8PT0BAAEBAbC2tsahQ4ekmJycHJw/f15KIkJCQqBUKnHq1Ckp5uTJk1AqlVJMdbAlgoiISAdzPDvjzp07+PXXX6XXmZmZSEtLg7OzM5ydnbFkyRKMGjUKnp6eyMrKwsKFC+Hq6opnnnkGAKBQKBAVFYW5c+fCxcUFzs7OmDdvHjp37izN1ujYsSMGDRqESZMmYf369QCAyZMnIyIiotozMwAmEURERLqZYYpnSkoK+vbtK72eM2cOAGDChAlYu3Ytzp07hy1btuDWrVvw9PRE3759sWvXLjg6OkrHrFq1ClZWVhg9ejSKi4vRr18/xMXFwdLSUorZtm0bZs+eLc3iGDZsmN61KaoiE0I08kU5tRUWFkKhUODpLvNhZWm65iSqnzRpF81dBSIyMZUowzF8BaVSqTXOwJQqviv8J8XA0sbW4POoS+/h/IaFtVpXc2JLBBERkQ58FLh+TCKIiIh04QO49OLsDCIiIjLII9sSoTl/CRqZtbmrQURE9Ri7M/R7ZJMIIiKih2J3hl5MIoiIiHRhEqEXx0QQERGRQdgSQUREpAPHROjHJIKIiEgXdmfoxe4MIiIiMghbIoiIiHSQCQGZEU+HMObYhoBJBBERkS7sztCL3RlERERkELZEEBER6cDZGfoxiSAiItKF3Rl6sTuDiIiIDMKWCCIiIh3YnaEfkwgiIiJd2J2hF5MIIiIiHdgSoR/HRBAREZFB2BJBRESkC7sz9GISQUREpEdj75IwBrsziIiIyCBsiSAiItJFiPLNmOMbMSYRREREOnB2hn7sziAiIiKDsCWCiIhIF87O0ItJBBERkQ4yTflmzPGNGbsziIiIyCBsiSAiItKF3Rl6MYkgIiLSgbMz9GMSQUREpAvXidCLYyKIiIjIIGyJICIi0oHdGfqxJYKIiEgXYYKthr7//nsMHToUXl5ekMlk+PLLL7WrJASWLFkCLy8v2NnZoU+fPrhw4YJWTElJCWbNmgVXV1c4ODhg2LBhuHr1qlZMQUEBIiMjoVAooFAoEBkZiVu3btWorkwiiIiI6pGioiJ07doVa9asqXL/ihUrsHLlSqxZswbJycnw8PBA//79cfv2bSkmOjoae/fuxc6dO3H8+HHcuXMHERERUKvVUszYsWORlpaG+Ph4xMfHIy0tDZGRkTWqK7sziIiIdDBHd0Z4eDjCw8Or3CeEwOrVq7Fo0SKMHDkSALB582a4u7tj+/btmDJlCpRKJTZu3IitW7ciLCwMAPD555/D29sbhw8fxsCBA5Geno74+HgkJSUhKCgIALBhwwaEhIQgIyMDfn5+1aorWyKIiIh0qZidYcwGoLCwUGsrKSkxqDqZmZnIzc3FgAEDpDK5XI7evXvjxIkTAIDU1FSUlZVpxXh5ecHf31+KSUxMhEKhkBIIAAgODoZCoZBiqoNJBBERUS3z9vaWxh4oFArExsYadJ7c3FwAgLu7u1a5u7u7tC83Nxc2NjZo1qyZ3hg3N7dK53dzc5NiqoPdGURERDqYqjsjOzsbTk5OUrlcLjeuXjKZ1mshRKWyBz0YU1V8dc5zP7ZEEBER6WKi2RlOTk5am6FJhIeHBwBUai3Iy8uTWic8PDxQWlqKgoICvTHXr1+vdP78/PxKrRz6MIkgIiJqIHx9feHh4YFDhw5JZaWlpUhISEBoaCgAICAgANbW1loxOTk5OH/+vBQTEhICpVKJU6dOSTEnT56EUqmUYqqD3RlEREQ6mGN2xp07d/Drr79KrzMzM5GWlgZnZ2e0atUK0dHRiImJQbt27dCuXTvExMTA3t4eY8eOBQAoFApERUVh7ty5cHFxgbOzM+bNm4fOnTtLszU6duyIQYMGYdKkSVi/fj0AYPLkyYiIiKj2zAyASQQREZFuGlG+GXN8DaWkpKBv377S6zlz5gAAJkyYgLi4OLz66qsoLi7G9OnTUVBQgKCgIBw8eBCOjo7SMatWrYKVlRVGjx6N4uJi9OvXD3FxcbC0tJRitm3bhtmzZ0uzOIYNG6ZzbQpdZEI08qeDPKCwsBAKhQJ9LEbCSmZt7upQbdOoHx5DRA2KSpThGL6CUqnUGqxoShXfFaFhS2FlbWvweVRl93Di8OJaras5cUwEERERGYTdGURERDrIYOSYCJPVpH5iEkFERKTLfatOGnx8I8buDCIiIjIIWyKIiIh0MMcUz4aESQQREZEu9606afDxjRi7M4iIiMggbIkgIiLSQSYEZEYMjjTm2IaASQQREZEumv9txhzfiLE7g4iIiAzClggiIiId2J2hH5MIIiIiXTg7Qy8mEURERLpwxUq9OCaCiIiIDMKWiEYkIjIfQ/6RD/eWpQCAy7/YYdtqD6QcVQAAmrqWIWrhHwh46jYcFCqcP+mIj15riWuZhj/mluqfiAk38Oy0fDi7leHyL7ZY97oXzp9qYu5qUS3gva59XLFSP7ZENCL5Odb4LLYFZg3ugFmDO+CnH5tgycbf4dO+GIDA4o2/w7NVKZZEtcGMgR1x/aoNlu34FXI7tbmrTibSe1gBpi69hh0fuGH6gPY4f9IBb23LRPMWpeauGpkY73UdqejOMGZrxMyaRMTGxqJnz55wdHSEm5sbRowYgYyMjIcel5CQgICAANja2qJNmzZYt25dHdS2/jt5uCmSjyjwR6Yt/si0RdyKFrh31wIdehShhW8JOgUU4cOF3vjlJwdc/d0WaxZ6w85Bjb4jCsxddTKRkZNv4Lsdzojf7oLsX22xbnEL5F+zRsQ/bpq7amRivNdUH5g1iUhISMCMGTOQlJSEQ4cOQaVSYcCAASgqKtJ5TGZmJgYPHoxevXrhzJkzWLhwIWbPno3du3fXYc3rPwsLgd7D/oTcToP0VAdYy8uz4dKSv265RiNDWakMj/e8Y65qkglZWWvQrstdpCY4apWnJjiiU6Du3ylqeHiv645MY/zWmJl1TER8fLzW602bNsHNzQ2pqal46qmnqjxm3bp1aNWqFVavXg0A6NixI1JSUvDuu+9i1KhRtV3leq91h2Ks/ioDNnINioss8cakNrhyyQ6WVgK52TZ48V9/4P1/tcK9uxYYOTkPLu4qOLuVmbvaZAJOzmpYWgG3bmj/Wt/Kt0IzN5WZakW1gfe6DnF2hl71akyEUqkEADg7O+uMSUxMxIABA7TKBg4ciJSUFJSVVf4yLCkpQWFhodbWmF39TY7pAzvgn8P88PVWV8xbdRmt2hVDrZLhzclt0KJNCXZfOIt9l9LQNeQOTh1xgkYjM3e1yYQe/D9LJkOjn6v+qOK9JnOrN7MzhBCYM2cOnnzySfj7++uMy83Nhbu7u1aZu7s7VCoVbty4AU9PT619sbGxWLp0aa3UuT5SlVngWlb5bItLZx3g1/UuRkTl44N/tcKv5+wxfWBH2DuqYW2tgfJPa7y//2f88pO9mWtNplD4pyXUKqBZc+2/RBWuKhTk15tfdTIB3us6xMWm9Ko3LREzZ87E2bNnsWPHjofGymTafzmL/6XjD5YDwIIFC6BUKqUtOzvbNBVuKGSAtY12p9zd25ZQ/mkNL997aNflLhIPNjVP3cikVGUWuHTWHj2euq1V3uOp27iY4mCmWlFt4L2uOxXLXhuzNWb1ImWdNWsW9u3bh++//x4tW7bUG+vh4YHc3Fytsry8PFhZWcHFxaVSvFwuh1wuN2l966sX5v+B5KMK5F+zhl0TDfoM+xNdQm7j3+PbAgB6DSmA8k8r5P1hA98OxZi69CoSv2uK0987mbnmZCp7PnHFKx9k45ezdkhPccDg8Tfh1qIM32yp/LtBDRvvNdUHZk0ihBCYNWsW9u7di2PHjsHX1/ehx4SEhGD//v1aZQcPHkRgYCCsra1rq6oNQtPmKrzyfhac3cpw97YlMtPt8O/xbXH6h/Ikwdm9DFMWX0VTVxX+zLPG4f84Y/v7HmauNZlSwr5mcGymxriXr8PZTYXLGbb493hf5P1hY+6qkYnxXtcRDqzUSyaE+d7h9OnTsX37dnz11Vfw8/OTyhUKBezs7ACUd0f88ccf2LJlC4DyKZ7+/v6YMmUKJk2ahMTEREydOhU7duyo1uyMwsJCKBQK9LEYCSvZo510PBI0XEiLqLFRiTIcw1dQKpVwcqqdltSK74q+PRbAytLwVX1V6ns4ejq2VutqTmYdE7F27VoolUr06dMHnp6e0rZr1y4pJicnB1euXJFe+/r64sCBAzh27Bi6deuGN998Ex988AGndxIRkclxTIR+Zu/OeJi4uLhKZb1798bp06droUZERERUXfViYCUREVG9JGDkmAiT1aReYhJBRESkCwdW6lVv1okgIiKihoUtEURERLpoABjzZAA+gIuIiOjRZOwMi8Y+O4PdGURERGQQJhFERES6VAysNGargSVLlkAmk2ltHh5/rSwshMCSJUvg5eUFOzs79OnTBxcuXNA6R0lJCWbNmgVXV1c4ODhg2LBhuHr1qkk+jgcxiSAiItKljpMIAHj88ceRk5MjbefOnZP2rVixAitXrsSaNWuQnJwMDw8P9O/fH7dv//UwtujoaOzduxc7d+7E8ePHcefOHURERECtNv0KvhwTQUREVI9YWVlptT5UEEJg9erVWLRoEUaOHAkA2Lx5M9zd3bF9+3ZMmTIFSqUSGzduxNatWxEWFgYA+Pzzz+Ht7Y3Dhw9j4MCBJq0rWyKIiIh0MVFLRGFhodZWUlKi85KXLl2Cl5cXfH198dxzz+H3338HUP7sqNzcXAwYMECKlcvl6N27N06cOAEASE1NRVlZmVaMl5cX/P39pRhTYhJBRESki8YEGwBvb28oFAppi42NrfJyQUFB2LJlC7777jts2LABubm5CA0Nxc2bN5GbmwsAcHd31zrG3d1d2pebmwsbGxs0a9ZMZ4wpsTuDiIhIB1NN8czOztZ6iqdcLq8yPjw8XPp3586dERISgsceewybN29GcHBw+Tll2gtXCCEqlT2oOjGGYEsEERFRLXNyctLadCURD3JwcEDnzp1x6dIlaZzEgy0KeXl5UuuEh4cHSktLUVBQoDPGlJhEEBER6WKG2Rn3KykpQXp6Ojw9PeHr6wsPDw8cOnRI2l9aWoqEhASEhoYCAAICAmBtba0Vk5OTg/Pnz0sxpsTuDCIiIl00ApAZkQhoanbsvHnzMHToULRq1Qp5eXl46623UFhYiAkTJkAmkyE6OhoxMTFo164d2rVrh5iYGNjb22Ps2LEAAIVCgaioKMydOxcuLi5wdnbGvHnz0LlzZ2m2hikxiSAiIqonrl69iueffx43btxA8+bNERwcjKSkJPj4+AAAXn31VRQXF2P69OkoKChAUFAQDh48CEdHR+kcq1atgpWVFUaPHo3i4mL069cPcXFxsLS0NHl9ZUI08oW9H1BYWAiFQoE+FiNhJbM2d3WotmlMv7gKEZmXSpThGL6CUqnUGqxoShXfFWFt/gkry+qNX6iKSl2Cw7+/X6t1NSe2RBAREelk7LiGxv13OgdWEhERkUHYEkFERKSLsTMsGvmIASYRREREumgEjOqSqOHsjIaG3RlERERkELZEEBER6SI05ZsxxzdiTCKIiIh04ZgIvZhEEBER6cIxEXpxTAQREREZhC0RREREurA7Qy8mEURERLoIGJlEmKwm9RK7M4iIiMggbIkgIiLShd0ZejGJICIi0kWjAWDEWg+axr1OBLsziIiIyCBsiSAiItKF3Rl6MYkgIiLShUmEXuzOICIiIoOwJYKIiEgXLnutF5MIIiIiHYTQQBjxJE5jjm0ImEQQERHpIoRxrQkcE0FERERUGVsiiIiIdBFGjolo5C0RTCKIiIh00WgAmRHjGhr5mAh2ZxAREZFB2BJBRESkC7sz9GISQUREpIPQaCCM6M5o7FM82Z1BREREBmFLBBERkS7sztCLSQQREZEuGgHImETowu4MIiIiMghbIoiIiHQRAoAx60Q07pYIJhFEREQ6CI2AMKI7QzTyJILdGURERLoIjfGbAT7++GP4+vrC1tYWAQEB+OGHH0z8xkyDSQQREVE9smvXLkRHR2PRokU4c+YMevXqhfDwcFy5csXcVauESQQREZEOQiOM3mpq5cqViIqKwksvvYSOHTti9erV8Pb2xtq1a2vhHRqHSQQREZEuddydUVpaitTUVAwYMECrfMCAAThx4oQp35lJPHIDKysGuahEmZlrQnVCqM1dAyIyMRXK//+ui0GLKpQZtdZURV0LCwu1yuVyOeRyeaX4GzduQK1Ww93dXavc3d0dubm5hlekljxyScTt27cBAMfFfqN+MIiIyLxu374NhUJRK+e2sbGBh4cHjuceMPpcTZo0gbe3t1bZ4sWLsWTJEp3HyGQyrddCiEpl9cEjl0R4eXkhOzsbjo6O9fKG1JbCwkJ4e3sjOzsbTk5O5q4O1SLe60fHo3qvhRC4ffs2vLy8au0atra2yMzMRGlpqdHnqioBqKoVAgBcXV1haWlZqdUhLy+vUutEffDIJREWFhZo2bKluathNk5OTo/UfzaPMt7rR8ejeK9rqwXifra2trC1ta3169zPxsYGAQEBOHToEJ555hmp/NChQxg+fHid1qU6HrkkgoiIqD6bM2cOIiMjERgYiJCQEHzyySe4cuUKpk6dau6qVcIkgoiIqB4ZM2YMbt68iTfeeAM5OTnw9/fHgQMH4OPjY+6qVcIk4hEhl8uxePFinf1w1HjwXj86eK8br+nTp2P69OnmrsZDyURjX9ibiIiIagUXmyIiIiKDMIkgIiIigzCJICIiIoMwiSAiIiKDMIloRGr6/PmEhAQEBATA1tYWbdq0wbp16+qopmSM77//HkOHDoWXlxdkMhm+/PLLhx7De93wxMbGomfPnnB0dISbmxtGjBiBjIyMhx7He011iUlEI1HT589nZmZi8ODB6NWrF86cOYOFCxdi9uzZ2L17dx3XnGqqqKgIXbt2xZo1a6oVz3vdMCUkJGDGjBlISkrCoUOHoFKpMGDAABQVFek8hvea6pygRuGJJ54QU6dO1Srr0KGD+Ne//lVl/Kuvvio6dOigVTZlyhQRHBxca3Uk0wMg9u7dqzeG97pxyMvLEwBEQkKCzhjea6prbIloBAx5/nxiYmKl+IEDByIlJQVlZXxMemPCe904KJVKAICzs7POGN5rqmtMIhoBQ54/n5ubW2W8SqXCjRs3aq2uVPd4rxs+IQTmzJmDJ598Ev7+/jrjeK+prnHZ60akps+fryq+qnJq+HivG7aZM2fi7NmzOH78+ENjea+pLjGJaAQMef68h4dHlfFWVlZwcXGptbpS3eO9bthmzZqFffv24fvvv0fLli31xvJeU11jd0YjcP/z5+936NAhhIaGVnlMSEhIpfiDBw8iMDAQ1tbWtVZXqnu81w2TEAIzZ87Enj17cOTIEfj6+j70GN5rqnPmHNVJprNz505hbW0tNm7cKC5evCiio6OFg4ODyMrKEkII8a9//UtERkZK8b///ruwt7cXL7/8srh48aLYuHGjsLa2Fv/5z3/M9Raomm7fvi3OnDkjzpw5IwCIlStXijNnzojLly8LIXivG4tp06YJhUIhjh07JnJycqTt7t27UgzvNZkbk4hG5KOPPhI+Pj7CxsZG9OjRQ2sq2IQJE0Tv3r214o8dOya6d+8ubGxsROvWrcXatWvruMZkiKNHjwoAlbYJEyYIIXivG4uq7jEAsWnTJimG95rMjY8CJyIiIoNwTAQREREZhEkEERERGYRJBBERERmESQQREREZhEkEERERGYRJBBERERmESQQREREZhEkEkRksWbIE3bp1k15PnDgRI0aMqPN6ZGVlQSaTIS0tTWdM69atsXr16mqfMy4uDk2bNjW6bjKZDF9++aXR5yGi2sMkguh/Jk6cCJlMBplMBmtra7Rp0wbz5s1DUVFRrV/7/fffR1xcXLViq/PFT0RUF/gUT6L7DBo0CJs2bUJZWRl++OEHvPTSSygqKsLatWsrxZaVlZnsoUYKhcIk5yEiqktsiSC6j1wuh4eHB7y9vTF27FiMGzdOalKv6IL47LPP0KZNG8jlcgghoFQqMXnyZLi5ucHJyQlPP/00fvrpJ63zLlu2DO7u7nB0dERUVBTu3buntf/B7gyNRoPly5ejbdu2kMvlaNWqFd5++20AkJ7m2L17d8hkMvTp00c6btOmTejYsSNsbW3RoUMHfPzxx1rXOXXqFLp37w5bW1sEBgbizJkzNf6MVq5cic6dO8PBwQHe3t6YPn067ty5Uynuyy+/RPv27WFra4v+/fsjOztba//+/fsREBAAW1tbtGnTBkuXLoVKpapxfYjIfJhEEOlhZ2eHsrIy6fWvv/6KL774Art375a6E4YMGYLc3FwcOHAAqamp6NGjB/r164c///wTAPDFF19g8eLFePvtt5GSkgJPT89KX+4PWrBgAZYvX47XXnsNFy9exPbt2+Hu7g6gPBEAgMOHDyMnJwd79uwBAGzYsAGLFi3C22+/jfT0dMTExOC1117D5s2bAQBFRUWIiIiAn58fUlNTsWTJEsybN6/Gn4mFhQU++OADnD9/Hps3b8aRI0fw6quvasXcvXsXb7/9NjZv3owff/wRhYWFeO6556T93333HcaPH4/Zs2fj4sWLWL9+PeLi4qREiYgaCDM/AIyo3pgwYYIYPny49PrkyZPCxcVFjB49WgghxOLFi4W1tbXIy8uTYv773/8KJycnce/ePa1zPfbYY2L9+vVCCCFCQkLE1KlTtfYHBQWJrl27VnntwsJCIZfLxYYNG6qsZ2ZmpgAgzpw5o1Xu7e0ttm/frlX25ptvipCQECGEEOvXrxfOzs6iqKhI2r927doqz3U/Hx8fsWrVKp37v/jiC+Hi4iK93rRpkwAgkpKSpLL09HQBQJw8eVIIIUSvXr1ETEyM1nm2bt0qPD09pdcAxN69e3Vel4jMj2MiiO7z9ddfo0mTJlCpVCgrK8Pw4cPx4YcfSvt9fHzQvHlz6XVqairu3LkDFxcXrfMUFxfjt99+AwCkp6dj6tSpWvtDQkJw9OjRKuuQnp6OkpIS9OvXr9r1zs/PR3Z2NqKiojBp0iSpXKVSSeMt0tPT0bVrV9jb22vVo6aOHj2KmJgYXLx4EYWFhVCpVLh37x6Kiorg4OAAALCyskJgYKB0TIcOHdC0aVOkp6fjiSeeQGpqKpKTk7VaHtRqNe7du4e7d+9q1ZGI6i8mEUT36du3L9auXQtra2t4eXlVGjhZ8SVZQaPRwNPTE8eOHat0LkOnOdrZ2dX4GI1GA6C8SyMoKEhrn6WlJQBACGFQfe53+fJlDB48GFOnTsWbb74JZ2dnHD9+HFFRUVrdPkD5FM0HVZRpNBosXboUI0eOrBRja2trdD2JqG4wiSC6j4ODA9q2bVvt+B49eiA3NxdWVlZo3bp1lTEdO3ZEUlIS/vGPf0hlSUlJOs/Zrl072NnZ4b///S9eeumlSvttbGwAlP/lXsHd3R0tWrTA77//jnHjxlV53k6dOmHr1q0oLi6WEhV99ahKSkoKVCoV3nvvPVhYlA+p+uKLLyrFqVQqpKSk4IknngAAZGRk4NatW+jQoQOA8s8tIyOjRp81EdU/TCKIjBAWFoaQkBCMGDECy5cvh5+fH65du4YDBw5gxIgRCAwMxD//+U9MmDABgYGBePLJJ7Ft2zZcuHABbdq0qfKctra2mD9/Pl599VXY2Njgb3/7G/Lz83HhwgVERUXBzc0NdnZ2iI+PR8uWLWFrawuFQoElS5Zg9uzZcHJyQnh4OEpKSpCSkoKCggLMmTMHY8eOxaJFixAVFYV///vfyMrKwrvvvluj9/vYY49BpVLhww8/xNChQ/Hjjz9i3bp1leKsra0xa9YsfPDBB7C2tsbMmTMRHBwsJRWvv/46IiIi4O3tjWeffRYWFhY4e/Yszp07h7feeqvmN4KIzIKzM4iMIJPJcODAATz11FN48cUX0b59ezz33HPIysqSZlOMGTMGr7/+OubPn4+AgABcvnwZ06ZN03ve1157DXPnzsXrr7+Ojh07YsyYMcjLywNQPt7ggw8+wPr16+Hl5YXhw4cDAF566SV8+umniIuLQ+fOndG7d2/ExcVJU0KbNGmC/fv34+LFi+jevTsWLVqE5cuX1+j9duvWDStXrsTy5cvh7++Pbdu2ITY2tlKcvb095s+fj7FjxyIkJAR2dnbYuXOntH/gwIH4+uuvcejQIfTs2RPBwcFYuXIlfHx8alQfIjIvmTBFRykRERE9ctgSQURERAZhEkFEREQGYRJBREREBmESQURERAZhEkFEREQGYRJBREREBmESQURERAZhEkFEREQGYRJBREREBmESQURERAZhEkFEREQGYRJBREREBvl/shcGenQzvfIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation Metrics:\n",
      "Accuracy: 0.868\n",
      "Recall: 0.333\n",
      "Precision: 0.289\n",
      "F1 Score: 0.310\n",
      "AUC: 0.553\n"
     ]
    }
   ],
   "source": [
    "# 4. Logistic Regression\n",
    "\n",
    "# (1) Define variable groups\n",
    "binary_cols = [\"FLAG_FEMALE\", \"FLAG_OWN_CAR\", \"FLAG_OWN_REALTY\", \"FLAG_WORK_PHONE\", \"FLAG_PHONE\", \"FLAG_EMAIL\"]\n",
    "categorical_cols = [\"NAME_INCOME_TYPE\", \"NAME_EDUCATION_TYPE\", \"NAME_FAMILY_STATUS\", \"NAME_HOUSING_TYPE\", \"OCCUPATION_TYPE\"]\n",
    "numeric_cols = [\"CNT_CHILDREN\", \"AMT_INCOME_TOTAL\", \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"CNT_FAM_MEMBERS\"]\n",
    "\n",
    "# (2) Data preprocessing\n",
    "df = credit_application.copy()\n",
    "df = df[[\"credit\"] + binary_cols + categorical_cols + numeric_cols].dropna()\n",
    "\n",
    "# Convert target variable to category, use 'medium' as reference\n",
    "df[\"credit\"] = pd.Categorical(df[\"credit\"], categories=[\"medium\", \"good\", \"bad\"], ordered=False)\n",
    "y = df[\"credit\"].cat.codes.astype(float)  # medium=0, good=1, bad=2\n",
    "\n",
    "# Encode categorical features\n",
    "X_cat = pd.get_dummies(df[categorical_cols], drop_first=True)\n",
    "\n",
    "# Standardize numeric features\n",
    "scaler = StandardScaler()\n",
    "X_num = pd.DataFrame(scaler.fit_transform(df[numeric_cols]), columns=numeric_cols, index=df.index)\n",
    "\n",
    "# Combine all features\n",
    "X_bin = df[binary_cols].astype(float)\n",
    "X = pd.concat([X_num, X_bin, X_cat], axis=1).astype(float)\n",
    "X = sm.add_constant(X)  # Add constant term\n",
    "\n",
    "# (3) Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)\n",
    "\n",
    "# (4) Fit multinomial logistic regression\n",
    "model = MNLogit(y_train, X_train)\n",
    "result = model.fit(method='newton', maxiter=100, disp=True)\n",
    "print(result.summary())\n",
    "\n",
    "# (5) Model evaluation: Likelihood Ratio Chi-square, P-value, AIC, BIC\n",
    "ll_null = model.fit(disp=0).llnull\n",
    "ll_full = result.llf\n",
    "lr_stat = 2 * (ll_full - ll_null)\n",
    "lr_pvalue = stats.chi2.sf(lr_stat, df=result.df_model)\n",
    "\n",
    "print(\"\\nModel Evaluation:\")\n",
    "print(f\"Likelihood Ratio Chi-square: {lr_stat:.3f}\")\n",
    "print(f\"P-value: {lr_pvalue:.5f}\")\n",
    "print(f\"AIC: {result.aic:.3f}\")\n",
    "print(f\"BIC: {result.bic:.3f}\")\n",
    "\n",
    "# (6) Model prediction\n",
    "pred_prob = result.predict(X_test)\n",
    "y_pred = pred_prob.idxmax(axis=1).astype(float)\n",
    "\n",
    "# (7) Confusion matrix\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
    "plt.title(\"Confusion Matrix - Multinomial Logit\")\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "# (8) Performance metrics\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred, average='macro', zero_division=0)\n",
    "pre = precision_score(y_test, y_pred, average='macro', zero_division=0)\n",
    "f1 = f1_score(y_test, y_pred, average='macro', zero_division=0)\n",
    "auc = roc_auc_score(pd.get_dummies(y_test), pred_prob, multi_class='ovr')\n",
    "\n",
    "print(\"\\nEvaluation Metrics:\")\n",
    "print(f\"Accuracy: {acc:.3f}\")\n",
    "print(f\"Recall: {rec:.3f}\")\n",
    "print(f\"Precision: {pre:.3f}\")\n",
    "print(f\"F1 Score: {f1:.3f}\")\n",
    "print(f\"AUC: {auc:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44851cc",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 5. Random Forest Classification\n",
    "\n",
    "### 5.1 Model Introduction\n",
    "\n",
    "#### **Principle :**\n",
    "\n",
    "- Random Forest is an ensemble algorithm that combines multiple decision trees\n",
    "- Each tree is trained on a bootstrap sample of the data\n",
    "- a random subset of features is considered at each node split\n",
    "- Final predictions are made by majority vote.\n",
    "\n",
    "#### **Strengths :**\n",
    "\n",
    "- high accuracy\n",
    "- resilience to noisy or missing data\n",
    "- suitability for high-dimensional tasks\n",
    "#### **drawbacks :**\n",
    "\n",
    "- limited interpretability compared to linear models\n",
    "- higher computation cost\n",
    "- required careful hyperparameter tuning\n",
    "\n",
    "### 5.2 Algorithm Highlights\n",
    "#### (1) Handling Class Imbalance with SMOTENC\n",
    "\n",
    "This project used  **SMOTENC** (Synthetic Minority Over-sampling Technique for Nominal and Continuous features) to address sample imbalance. Unlike standard SMOTE, SMOTENC supports mixed-type data:\n",
    "\n",
    "- **Numerical features** (e.g., income, age)\n",
    "- **Binary indicators** (e.g., gender, home ownership)\n",
    "- **Categorical variables** (e.g., education level, occupation)\n",
    "\n",
    "SMOTENC generates synthetic samples using nearest-neighbor sampling, while avoiding interpolation across categorical values. This approach helps:\n",
    "\n",
    "- Preserve feature integrity for non-numeric data\n",
    "- Improve recall on underrepresented classes such as “bad” credit\n",
    "- Avoid overfitting and maintain model generalization\n",
    "\n",
    "Importantly, oversampling is applied **only on the training set within the pipeline**, preventing data leakage.\n",
    "\n",
    "#### (2) Integrated Hyperparameter Optimization\n",
    "\n",
    "The pipeline incorporates **GridSearchCV** to perform automated hyperparameter tuning with cross-validation. Key advantages include:\n",
    "\n",
    "- Simultaneous optimization of sampling strategy and classifier performance\n",
    "- Use of **macro F1** as a scoring metric, ensuring fairness across imbalanced classes\n",
    "- Avoidance of manual trial-and-error in model selection\n",
    "\n",
    "This ensures robust and scalable parameter tuning tailored to the specific prediction task.\n",
    "\n",
    "#### (3) Encapsulated Model Training and Evaluation\n",
    "\n",
    "The pipeline is functionally modular, enhancing clarity and reusability:\n",
    "\n",
    "- `build_and_optimize_model()` constructs the full training pipeline, including preprocessing, oversampling, and classifier fitting.\n",
    "- `evaluate_model()` handles consistent evaluation across **train**, **validation (CV)**, and **test** datasets.\n",
    "\n",
    "Evaluation is thorough and includes:\n",
    "\n",
    "- Standard metrics: accuracy, precision, recall, F1, and AUC\n",
    "- **Visualization** tools: confusion matrix and multi-class ROC curves\n",
    "- Support for both class labels and probability scores\n",
    "\n",
    "This setup enables systematic comparison across models and ensures **reproducibility** and **scalability** in practical applications.\n",
    "\n",
    "\n",
    "### 5.3 Evaluation Results\n",
    "\n",
    "#### **Confusion Matrix :**\n",
    "The confusion matrix reveals the random forest model's robust performance in credit risk classification, particularly for the dominant \"medium\" class, with 3557 accurate predictions. Although 179 and 435 instances are misclassified as \"bad\" and \"good\" respectively, the model's proficiency in recognizing the prevalent risk category is evident. For the \"good\" class, it correctly predicts 278 cases, yet mislabels 297 as \"medium\". The \"bad\" class sees only 12 correct identifications. Despite challenges in classifying less frequent categories, the model's overall effectiveness in handling the majority class underscores its practical utility in credit risk assessment.\n",
    "\n",
    "<img src=\"confus_rf.png\" alt=\"confus_rf\" width=\"600\"> \n",
    "\n",
    "#### **Performance Metrics :**\n",
    "\n",
    "##### (1) Model Evaluation Results \n",
    "\n",
    "Following effective mitigation of class imbalance, the random forest model demonstrates robust predictive performance. On the training set, it achieves an accuracy of 0.8618, recall of 0.7529, and AUC of 0.9094—underscoring its proficiency in capturing complex feature interactions. The cross-validation (CV) and test sets both maintain accuracies exceeding 0.80, with their recall and F1-score profiles reflecting consistent classification robustness across distinct data partitions. While marginal generalization discrepancies persist, the test set’s AUC of 0.7304 corroborates the model’s sustained capacity for class discrimination. \n",
    "\n",
    "| Dataset&#xA;         | Accuracy&#xA; | Recall&#xA;   | Precision&#xA; | F1&#xA;       | AUC&#xA;      |\n",
    "| -------------------- | ------------- | ------------- | -------------- | ------------- | ------------- |\n",
    "| Train&#xA;           | 0.861836&#xA; | 0.752885&#xA; | 0.550246&#xA;  | 0.589248&#xA; | 0.909374&#xA; |\n",
    "| Validation (CV)&#xA; | 0.805872&#xA; | 0.521838&#xA; | 0.448866&#xA;  | 0.461620&#xA; | 0.708057&#xA; |\n",
    "| Test&#xA;            | 0.800958&#xA; | 0.543096&#xA; | 0.453702&#xA;  | 0.468028&#xA; | 0.730394&#xA; |\n",
    "\n",
    "##### (2) Test Set Classification Report \n",
    "\n",
    "On the training set, the random forest model attains an accuracy of 0.8618, a recall of 0.7529, and an AUC of 0.9094, a testament to its adeptness at discerning intricate feature relationships. The cross-validation (CV) and test sets maintain accuracies north of 0.80, with their recall and F1-score metrics exemplifying the model's unwavering classification stability across diverse data distributions. Although minor generalization variances are observable, the test set's AUC of 0.7304 firmly validates its consistent ability to distinguish between classes. \n",
    "\n",
    "|                   | precision&#xA; | recall&#xA; | f1-score&#xA; | support&#xA; |\n",
    "| ----------------- | -------------- | ----------- | ------------- | ------------ |\n",
    "| bad&#xA;          | 0.06&#xA;      | 0.31&#xA;   | 0.10&#xA;     | 39&#xA;      |\n",
    "| good&#xA;         | 0.39&#xA;      | 0.47&#xA;   | 0.42&#xA;     | 593&#xA;     |\n",
    "| medium&#xA;       | 0.92&#xA;      | 0.85&#xA;   | 0.88&#xA;     | 4171&#xA;    |\n",
    "| accuracy&#xA;     |                |             | 0.80&#xA;     | 4803&#xA;    |\n",
    "| macro avg&#xA;    | 0.45&#xA;      | 0.54&#xA;   | 0.47&#xA;     | 4803&#xA;    |\n",
    "| weighted avg&#xA; | 0.85&#xA;      | 0.80&#xA;   | 0.82&#xA;     | 4803&#xA;    |\n",
    "\n",
    "##### (3) The ROC Curve \n",
    "\n",
    "The ROC curve shows the random forest model performs best for the medium credit class (steep green curve), with more moderate but acceptable discrimination for good and bad. Despite challenges with minority class samples, the model balances true/false positive rates reasonably well, especially for the dominant medium category.\n",
    "\n",
    "<img src=\"roc_rf.png\" alt=\"roc_rf\" width=\"600\"> \n",
    "\n",
    "\n",
    "### 5.4 Feature Importance \n",
    "\n",
    "The \"Feature Importances\" chart highlights `OCCUPATION_TYPE` (0.179) as the most influential predictor, underscoring its economic significance in assessing credit risk through income stability and industry resilience. `DAYS_BIRTH` (0.117) and `DAYS_EMPLOYED` (0.112) follow, reflecting life-cycle and employment stability—key indicators in credit economics. `AMT_INCOME_TOTAL` (0.106) ranks fourth, directly linking repayment capacity to income. These top four features, totaling ~51.4% importance, emphasize occupational and economic factors as primary drivers. Other variables (e.g., family status) contribute minimally (<0.07), aligning with economic theory that career, age, and income are foundational to financial behavior.\n",
    "\n",
    "<img src=\"feature_rf.png\" alt=\"feature_rf\" width=\"600\"> \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ebdbdc52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5&6 General Functions in Machine Learning\n",
    "\n",
    "y = credit_application['credit']\n",
    "X = credit_application.drop(columns=['credit'])\n",
    "\n",
    "# (1) Categorical variable splitting and processing\n",
    "\n",
    "# Binary variables (0-1)\n",
    "binary_cols = [\"FLAG_FEMALE\", \"FLAG_OWN_CAR\", \"FLAG_OWN_REALTY\", \"FLAG_WORK_PHONE\", \"FLAG_PHONE\", \"FLAG_EMAIL\"]\n",
    "# Categorical variables (categories > 2)\n",
    "categorical_cols = [\"NAME_INCOME_TYPE\", \"NAME_EDUCATION_TYPE\", \"NAME_FAMILY_STATUS\", \"NAME_HOUSING_TYPE\", \"OCCUPATION_TYPE\"]\n",
    "# Continuous numerical variables\n",
    "numeric_cols = [\"CNT_CHILDREN\", \"AMT_INCOME_TOTAL\", \"DAYS_BIRTH\", \"DAYS_EMPLOYED\", \"CNT_FAM_MEMBERS\"] \n",
    "\n",
    "# Build preprocessing pipeline: standardization, preserve binary variables, one-hot encoding\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numeric_cols),           # Standardize continuous variables\n",
    "        ('bin', 'passthrough', binary_cols),               # Pass binary variables through\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols)  # One-hot encode categorical variables\n",
    "    ])\n",
    "\n",
    "# (2) Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)\n",
    "\n",
    "# (3) define function to build and optimize model\n",
    "def build_and_optimize_model(name, model, param_grid):\n",
    "    # Fit preprocessor to get categorical feature indices\n",
    "    preprocessor.fit(X_train)\n",
    "    cat_encoder = preprocessor.named_transformers_['cat']\n",
    "    cat_ohe_dim = cat_encoder.get_feature_names_out().shape[0]\n",
    "    categorical_feature_indices = list(range(len(numeric_cols), len(numeric_cols) + len(binary_cols) + cat_ohe_dim))\n",
    "\n",
    "    # Build pipeline with SMOTENC (fix n_jobs error)\n",
    "    pipeline = ImbPipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('smote', SMOTENC(categorical_features=categorical_feature_indices, random_state=42)),\n",
    "        ('classifier', model)\n",
    "    ])\n",
    "\n",
    "    grid = GridSearchCV(pipeline, param_grid, cv=5, scoring='f1_macro', n_jobs=-1)\n",
    "    grid.fit(X_train, y_train)\n",
    "    return grid\n",
    "\n",
    "# (4) Model evaluation and visualization\n",
    "def evaluate_model(name, model):\n",
    "    sets = {\n",
    "        \"Train\": (X_train, y_train),\n",
    "        \"Validation (CV)\": (X_train, y_train),  # Use cross-validation for evaluation\n",
    "        \"Test\": (X_test, y_test)\n",
    "    }\n",
    "\n",
    "    metrics = []\n",
    "\n",
    "    for set_name, (X_set, y_set) in sets.items():\n",
    "        if set_name == \"Validation (CV)\":\n",
    "            y_pred = cross_val_predict(model, X_set, y_set, cv=5)\n",
    "            if hasattr(model.best_estimator_.named_steps['classifier'], 'predict_proba'):\n",
    "                y_score = cross_val_predict(model, X_set, y_set, cv=5, method='predict_proba')\n",
    "        else:\n",
    "            y_pred = model.predict(X_set)\n",
    "            if hasattr(model.best_estimator_.named_steps['classifier'], 'predict_proba'):\n",
    "                y_score = model.predict_proba(X_set)\n",
    "            else:\n",
    "                y_score = None\n",
    "\n",
    "        acc = accuracy_score(y_set, y_pred)\n",
    "        rec = recall_score(y_set, y_pred, average='macro')\n",
    "        pre = precision_score(y_set, y_pred, average='macro')\n",
    "        f1 = f1_score(y_set, y_pred, average='macro')\n",
    "\n",
    "        if y_score is not None:\n",
    "            try:\n",
    "                auc = roc_auc_score(pd.get_dummies(y_set), y_score, average='macro', multi_class='ovr')\n",
    "            except:\n",
    "                auc = np.nan\n",
    "        else:\n",
    "            auc = np.nan\n",
    "\n",
    "        metrics.append([set_name, acc, rec, pre, f1, auc])\n",
    "\n",
    "    df_metrics = pd.DataFrame(metrics, columns=[\"Dataset\", \"Accuracy\", \"Recall\", \"Precision\", \"F1\", \"AUC\"])\n",
    "    print(f\"\\n{name} Model Evaluation Results:\")\n",
    "    print(df_metrics.to_string(index=False))\n",
    "\n",
    "    # Print classification report for test set\n",
    "    print(f\"\\n{name} - Test Set Classification Report:\\n\")\n",
    "    print(classification_report(y_test, model.predict(X_test)))\n",
    "\n",
    "    # Confusion matrix\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(model, X_test, y_test)\n",
    "    disp.ax_.set_title(f\"Confusion Matrix - {name}\")\n",
    "    plt.grid(False)\n",
    "    plt.show()\n",
    "\n",
    "    # Multi-class ROC curve\n",
    "    if hasattr(model.best_estimator_.named_steps['classifier'], 'predict_proba'):\n",
    "        y_score = model.predict_proba(X_test)\n",
    "        classes = model.best_estimator_.named_steps['classifier'].classes_\n",
    "        for i, cls in enumerate(classes):\n",
    "            fpr, tpr, _ = roc_curve((y_test == cls).astype(int), y_score[:, i])\n",
    "            plt.plot(fpr, tpr, label=f\"Class {cls}\")\n",
    "        plt.title(f\"ROC Curve - {name}\")\n",
    "        plt.xlabel(\"False Positive Rate\")\n",
    "        plt.ylabel(\"True Positive Rate\")\n",
    "        plt.legend()\n",
    "        plt.grid(True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57cb2694",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best RF params: {'classifier__max_depth': 16, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 190}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Model Evaluation Results:\n",
      "        Dataset  Accuracy   Recall  Precision       F1      AUC\n",
      "          Train  0.862616 0.751054   0.551140 0.591172 0.909275\n",
      "Validation (CV)  0.804831 0.521920   0.448248 0.461221 0.708698\n",
      "           Test  0.802832 0.544780   0.454353 0.470854 0.728690\n",
      "\n",
      "Random Forest - Test Set Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         bad       0.06      0.31      0.11        39\n",
      "        good       0.38      0.47      0.42       593\n",
      "      medium       0.92      0.85      0.89      4171\n",
      "\n",
      "    accuracy                           0.80      4803\n",
      "   macro avg       0.45      0.54      0.47      4803\n",
      "weighted avg       0.85      0.80      0.82      4803\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Random Forest Algorithm for Classification\n",
    "\n",
    "# 5.1 build and optimize model\n",
    "\n",
    "# To save running time, we directly use the Best RF params obtained from the search \n",
    "rf_param_grid = {\n",
    "    'classifier__n_estimators': [190],\n",
    "    'classifier__max_depth': [16],\n",
    "    'classifier__min_samples_split': [2]\n",
    "}\n",
    "\n",
    "# rf_param_grid = {\n",
    "#     'classifier__n_estimators': range(100, 200, 10),\n",
    "#     'classifier__max_depth': range(4, 20, 4),\n",
    "#     'classifier__min_samples_split': range(2, 5, 1)\n",
    "# }\n",
    "\n",
    "rf_model = build_and_optimize_model(\"RandomForest\", RandomForestClassifier(random_state=42), rf_param_grid)\n",
    "print(\"Best RF params:\", rf_model.best_params_)\n",
    "\n",
    "# 5.2 evaluate model\n",
    "evaluate_model(\"Random Forest\", rf_model)\n",
    "\n",
    "# 5.3 get feature importances\n",
    "\n",
    "# Get components from the trained Pipeline\n",
    "trained_pipeline = rf_model.best_estimator_\n",
    "trained_preprocessor = trained_pipeline.named_steps['preprocessor']\n",
    "rf = trained_pipeline.named_steps['classifier']\n",
    "\n",
    "# Get feature names after preprocessing\n",
    "ohe = trained_preprocessor.named_transformers_['cat']\n",
    "ohe_feature_names = ohe.get_feature_names_out(categorical_cols)\n",
    "\n",
    "# Note: StandardScaler does not rename columns, so use numeric_cols directly\n",
    "all_features = (\n",
    "    numeric_cols +\n",
    "    binary_cols +\n",
    "    ohe_feature_names.tolist()\n",
    ")\n",
    "\n",
    "# Get feature importances and create DataFrame\n",
    "importances = rf.feature_importances_\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': all_features,\n",
    "    'Importance': importances\n",
    "})\n",
    "\n",
    "# Parse original feature name (group one-hot encoded features)\n",
    "def extract_original_feature(feature_name):\n",
    "    for col in categorical_cols:\n",
    "        if feature_name.startswith(col + \"_\"):\n",
    "            return col\n",
    "    if feature_name in numeric_cols:\n",
    "        return feature_name\n",
    "    if feature_name in binary_cols:\n",
    "        return feature_name\n",
    "    return \"Other\"\n",
    "\n",
    "feature_importance_df['OriginalFeature'] = feature_importance_df['Feature'].apply(extract_original_feature)\n",
    "\n",
    "# Aggregate feature importance by original variable\n",
    "grouped_importance = feature_importance_df.groupby('OriginalFeature')['Importance'].sum().reset_index()\n",
    "grouped_importance = grouped_importance.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "ax = sns.barplot(\n",
    "    data=grouped_importance.head(20), \n",
    "    x='Importance', \n",
    "    y='OriginalFeature', \n",
    "    palette='viridis',\n",
    "    hue='OriginalFeature', \n",
    "    legend=False            \n",
    ")\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.xlabel('Total Importance')\n",
    "plt.ylabel('Feature')\n",
    "\n",
    "for patch in ax.patches:\n",
    "    importance = patch.get_width()  \n",
    "    y_center = patch.get_y() + patch.get_height() / 2  \n",
    "    ax.text(\n",
    "        importance+0.0008,         \n",
    "        y_center,           \n",
    "        f\"{importance:.3f}\", \n",
    "        ha='left',          \n",
    "        va='center',        \n",
    "        fontsize=10,         \n",
    "        color='black',\n",
    "        fontweight='bold'\n",
    "    )\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91904183",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 6. Neural Network\n",
    "\n",
    "### 6.1 Model Introduction\n",
    "\n",
    "Neural networks are foundational to deep learning and are inspired by the structure of biological neurons. A standard neural network consists of an input layer, hidden layers, and an output layer. Data flows through these layers and is transformed nonlinearly via activation functions. The network learns by backpropagation—errors between predictions and true values are propagated backward to iteratively update the connection weights. This process minimizes the loss function and refines model performance.\n",
    "\n",
    "While neural networks excel at capturing complex, non-linear relationships and automatically learning feature representations, they also have limitations. Their lack of interpretability makes it difficult to understand decision logic, training often requires substantial computational resources, and performance can be highly sensitive to hyperparameter settings and input data quality.\n",
    "\n",
    "### 6.2 Hyperparameter Selection\n",
    "As with the random forest model, we applied GridSearchCV to perform hyperparameter tuning for the neural network. The optimal configuration obtained was:<br>\n",
    "`'classifier__alpha': 0.01`<br>\n",
    "`'classifier__hidden_layer_sizes': 128`<br>\n",
    "`'classifier__learning_rate_init': 0.05`\n",
    "\n",
    "### 6.3 Evaluation Results\n",
    "\n",
    "#### **Confusion Matrix :**\n",
    "\n",
    "The neural network model's confusion matrix demonstrates enhanced credit risk classification, especially in handling class imbalance. It accurately predicts 2942 \"medium\" cases, with 92 misclassified as \"bad\" and 1137 as \"good\". For \"good\" and \"bad\" classes, it correctly identifies 293 and 6 cases respectively. Compared to prior models, this one shows better class balance, effectively managing the majority \"medium\" class while improving minority class prediction, confirming its practical value in credit risk assessment. \n",
    "\n",
    "<img src=\"confus_nn.png\" alt=\"confus_nn\" width=\"600\"> \n",
    "\n",
    "#### **Performance Metrics :**\n",
    "\n",
    "##### (1) Model Evaluation Results \n",
    "\n",
    "The neural network model achieves accuracy scores of 0.707, 0.694, and 0.675 on training, validation, and test datasets, respectively. With an AUC of 0.762 on the training set, it shows strong discriminatory power, but reduced AUCs of 0.630 and 0.621 on validation and test sets suggest potential overfitting. Recall varies across datasets (0.560, 0.430, 0.451), while precision scores (0.436, 0.383, 0.388) remain moderately stable. F1-scores peak at 0.453 on the training set, confirming the trade-off between precision and recall. Despite robust handling of class imbalance, performance drops on unseen data, indicating a need for hyperparameter tuning or regularization. \n",
    "\n",
    "| Dataset&#xA;         | Accuracy&#xA; | Recall&#xA;   | Precision&#xA; | F1&#xA;       | AUC&#xA;      |\n",
    "| -------------------- | ------------- | ------------- | -------------- | ------------- | ------------- |\n",
    "| Train&#xA;           | 0.706700&#xA; | 0.560129&#xA; | 0.435795&#xA;  | 0.452778&#xA; | 0.761545&#xA; |\n",
    "| Validation (CV)&#xA; | 0.694362&#xA; | 0.430315&#xA; | 0.383309&#xA;  | 0.385419&#xA; | 0.630345&#xA; |\n",
    "| Test&#xA;            | 0.674787&#xA; | 0.451097&#xA; | 0.388015&#xA;  | 0.387656&#xA; | 0.621310&#xA; |\n",
    "\n",
    "##### (2) Test Set Classification Report \n",
    "\n",
    "The model excels in predicting the dominant \"medium\" class, achieving 0.90 precision and 0.79 F1-score, with 4171 samples. However, it struggles with the \"bad\" class (0.06 precision, 0.08 F1) due to its small support of 39 samples. For the \"good\" class, precision and F1 are 0.20 and 0.29 respectively. With an overall accuracy of 0.67, the weighted average metrics (0.81 precision, 0.72 F1) highlight the model's effectiveness, while the macro average reveals performance disparities across classes, indicating challenges in handling class imbalance. \n",
    "\n",
    "|                   | precision&#xA; | recall&#xA; | f1-score&#xA; | support&#xA; |\n",
    "| ----------------- | -------------- | ----------- | ------------- | ------------ |\n",
    "| bad&#xA;          | 0.06&#xA;      | 0.15&#xA;   | 0.08&#xA;     | 39&#xA;      |\n",
    "| good&#xA;         | 0.20&#xA;      | 0.49&#xA;   | 0.29&#xA;     | 593&#xA;     |\n",
    "| medium&#xA;       | 0.90&#xA;      | 0.71&#xA;   | 0.79&#xA;     | 4171&#xA;    |\n",
    "| accuracy&#xA;     |                |             | 0.67&#xA;     | 4803&#xA;    |\n",
    "| macro avg&#xA;    | 0.39&#xA;      | 0.45&#xA;   | 0.39&#xA;     | 4803&#xA;    |\n",
    "| weighted avg&#xA; | 0.81&#xA;      | 0.67&#xA;   | 0.72&#xA;     | 4803&#xA;    |\n",
    "\n",
    "##### (3) The ROC Curve \n",
    "\n",
    "The ROC curves show the neural network best discriminates \"medium\" (steep green curve), with \"good\" and \"bad\" also performing acceptably, balancing true/false positives. \n",
    "\n",
    "<img src=\"roc_nn.png\" alt=\"roc_nn\" width=\"600\"> \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77fe9236",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best NN params: {'classifier__alpha': 0.01, 'classifier__hidden_layer_sizes': 128, 'classifier__learning_rate_init': 0.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:484: FutureWarning: `BaseEstimator._check_n_features` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_n_features` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\bayes\\miniconda3\\envs\\test\\Lib\\site-packages\\sklearn\\base.py:493: FutureWarning: `BaseEstimator._check_feature_names` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation._check_feature_names` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Neural Network Model Evaluation Results:\n",
      "        Dataset  Accuracy   Recall  Precision       F1      AUC\n",
      "          Train  0.706700 0.560129   0.435795 0.452778 0.761545\n",
      "Validation (CV)  0.694362 0.430315   0.383309 0.385419 0.630345\n",
      "           Test  0.674787 0.451097   0.388015 0.387656 0.621310\n",
      "\n",
      "Neural Network - Test Set Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         bad       0.06      0.15      0.08        39\n",
      "        good       0.20      0.49      0.29       593\n",
      "      medium       0.90      0.71      0.79      4171\n",
      "\n",
      "    accuracy                           0.67      4803\n",
      "   macro avg       0.39      0.45      0.39      4803\n",
      "weighted avg       0.81      0.67      0.72      4803\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Neural Network\n",
    "\n",
    "# 6.1build and optimize the model\n",
    "\n",
    "# To save running time, we directly use the Best NN params obtained from the search \n",
    "nn_param_grid = {\n",
    "    'classifier__hidden_layer_sizes': [128],\n",
    "    'classifier__alpha': [0.01],\n",
    "    'classifier__learning_rate_init': [0.05]\n",
    "}\n",
    "\n",
    "# nn_param_grid = {\n",
    "#     'classifier__hidden_layer_sizes': [ (64, 32), (128, 64)],\n",
    "#     'classifier__alpha': [0.001, 0.01],\n",
    "#     'classifier__learning_rate_init': [0.001, 0.005, 0.01, 0.05]\n",
    "# }\n",
    "\n",
    "nn_model = build_and_optimize_model(\"NeuralNetwork\", MLPClassifier(max_iter=500, random_state=42), nn_param_grid)\n",
    "\n",
    "# 6.2 evaluate model\n",
    "print(\"Best NN params:\", nn_model.best_params_)\n",
    "evaluate_model(\"Neural Network\", nn_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d4e5bd",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "## 7. Conclusion and Recommendations\n",
    "\n",
    "This project demonstrates the comparative effectiveness of traditional and machine learning methods in modeling credit card default risk. Each method brings distinct strengths and limitations, offering valuable insight.\n",
    "\n",
    "### 7.1 Summary of Model Insights\n",
    "- **Multinomial Logistic Regression :** provide strong interpretability and highlighted classic economic indicators such as income, age, marital status, and employment. It excelled in explaining behavior but underperformed on minority class prediction due to linearity assumptions and class imbalance.\n",
    "\n",
    "- **Random Forest :** offer the best balance between prediction and feature importance. It identified nonlinear interactions and ranked predictors effectively, confirming the significance of age, work duration, and income. However, it faced interpretability challenges and struggled mildly with rare event detection.\n",
    "\n",
    "- **Neural Network :** capture subtle patterns in the data and performed consistently across splits, but it failed to meaningfully predict Bad credit due to skewed class distributions. Its economic utility is limited unless richer behavioral or longitudinal data are introduced.\n",
    "\n",
    "### 7.2 Key Economic Insights\n",
    "- **Stability Matters Most :** Across all models, employment duration and age were the strongest predictors. Stable work histories and middle-age demographics consistently correlated with lower default risk.\n",
    "\n",
    "- **Income and Family Structure :** Income level remains a major determinant, but its effect is moderated by household size and marital status. For example, single or widowed individuals tend to default more, potentially due to lower informal support.\n",
    "\n",
    "- **Gender Differences Exist :** Women exhibited marginally lower default risks, supporting previous findings on gendered financial behavior.\n",
    "\n",
    "- **High-Risk Groups :** Pensioners, unemployed individuals, and those without stable family situations should be flagged for additional review during credit assessment.\n",
    "\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437f6cb0",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
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